Reliability-Centered Maintenance and Mechanical Integrity Management for Aging Onshore Oil and Gas Production Facilities

References

[1] Health and Safety Executive. Plant ageing: Management of equipment containing hazardous fluids or pressure (research report RR509). HSE Books. 2006.

[2] Health and Safety Executive. Plant ageing study, phase 1 report (research report RR823). HSE Books. 2010.

[3] Shafiee M, Animah I. Life extension decision making of safety critical systems: An overview. Journal of Loss Prevention in the Process Industries. 2017;47:174–188.

[4] Animah I, Shafiee M. Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets. Journal of Loss Prevention in the Process Industries. 2018;53:17–28.

[5] Animah I, Shafiee M, Simms N, et al. Selection of the most suitable life extension strategy for ageing offshore assets using a life-cycle cost-benefit analysis approach. Journal of Quality in Maintenance Engineering. 2018;24(3):311–330.

[6] Azzam MM. Failure analysis of pipelines in the oil and gas industry. Pipeline Engineering: Design, Failure, and Management (IntechOpen). 2022. https://doi.org/10.5772/intechopen.108140

[7] Dai L, Wang D, Wang T. Analysis and comparison of long-distance pipeline failures. Journal of Petroleum Engineering. 2017;2017:3174636.

[8] Adebayo A, Dada AS. An evaluation of the causes of oil pipeline incidents in oil and gas industries in niger delta region of Nigeria. Journal of Engineering and Applied Sciences. 2008;3:279–281.

[9] Chang JI, Lin CC. A study of storage tank accidents. Journal of Loss Prevention in the Process Industries. 2006;19(1):51–59.

[10] Zheng B, Chen GH. Storage tank fire accidents. Process Safety Progress. 2011;30(3):291–293.

[11] Herbert I. The UK buncefield incident: The view from a UK risk assessment engineer. Journal of Loss Prevention in the Process Industries. 2010;23:913–920.

[12] Obetta PC. Reliability and integrity management of fired heaters, boilers and furnaces in upstream oil and gas: A review. Iconic Research and Engineering Journals. 2019;3(5):599–626. https://doi.org/10.64388/IREV3I5-1722580

[13] Asibor E, Isong DE. Risk-based process engineering framework for strengthening reliability across complex natural gas infrastructure systems. Iconic Research and Engineering Journals. 2019;3(2):1020–1060. https://doi.org/10.64388/IREV3I2-1723153

[14] Koch G, Varney J, Thompson N, Moghissi O. International measures of prevention, application, and economics of corrosion technologies study (IMPACT). NACE International. 2016.

[15] Campbell JD, Jardine AKS. Maintenance excellence: Optimizing equipment life-cycle decisions. CRC Press. 2001.

[16] Wan Mahmood WH, Ab Rahman MN, Deros BM, Mazli H. Maintenance management system for upstream operations in oil and gas industry: A case study. International Journal of Industrial and Systems Engineering. 2011;9(3):317–329.

[17] Alsyouf I. Maintenance practices in swedish industries: Survey results. International Journal of Production Economics. 2009;121(1):212–223.

[18] SAE International. Evaluation criteria for reliability-centered maintenance (RCM) processes (SAE JA1011). SAE International. 1999. https://doi.org/10.4271/JA1011_199908

[19] Emovon I, Norman RA, Murphy AJ. Elements of maintenance systems and tools for implementation within the framework of reliability centred maintenance: A review. Journal of Mechanical Engineering and Technology. 2016;8(2):1–34.

[20] Gupta G, Mishra RP. A SWOT analysis of reliability centered maintenance framework. Journal of Quality in Maintenance Engineering. 2016.

[21] Priyanta D, Siswantoro N, Sukandar RA. Determination of maintenance task on rotary equipment using reliability centered maintenance II method. International Journal of Marine Engineering Innovation and Research. 2019;4(3). https://doi.org/10.12962/J25481479.V4I3.5572

[22] Zou B, et al. Improved RCM method by AHP-FCE for the maintenance strategy of reciprocating compressor unit. Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications (ICIEA 2020). 2020;124–129. https://doi.org/10.1109/ICIEA48937.2020.9248219

[23] De Sanctis I, Paciarotti C, Di Giovine O. Integration between RCM and RAM: A case study. International Journal of Quality & Reliability Management. 2016;33(6):852–880. https://doi.org/10.1108/IJQRM-02-2015-0026

[24] American Petroleum Institute. API 510: Pressure vessel inspection code: In-service inspection, rating, repair, and alteration (11th ed.). American Petroleum Institute. 2022.

[25] American Petroleum Institute & American Society of Mechanical Engineers. API 579–1/ASME FFS-1: Fitness-for-service (3rd ed.). American Petroleum Institute / ASME. 2016.

[26] Barkhordari A, Mahmoudian Y, Yousefi F, Zarei P, Moradi B. Risk and asset integrated management with risk-based inspection technique in oil, gas, and petrochemical industries. Occupational Hygiene and Health Promotion Journal. 2020;4(3):223–239.

[27] Obetta PC. A risk-based inspection concept for fitness-for-service assessment of ageing pressure vessels on offshore joint venture facilities. Iconic Research and Engineering Journals. 2018;2(5):497–520. https://doi.org/10.64388/IREV2I5-1722578

[28] Alebrant Mendes A, Duarte Ribeiro JL. Establishment of a maintenance plan based on quantitative analysis in the context of RCM in a JIT production scenario. Reliability Engineering & System Safety. 2014;127:21–29. https://doi.org/10.1016/j.ress.2014.03.004

[29] Macchi M, Garetti M, Centrone D, Fumagalli L, Piero Pavirani G. Maintenance management of railway infrastructures based on reliability analysis. Reliability Engineering & System Safety. 2012;104:71–83.

[30] Atta S, Tofah P, Kankam MA, Adenuga OM. A critical review of NDT based integrity management and corrosion risk assessment strategies for refinery process equipment. International Journal of Engineering and Modern Technology. 2020;6(2):19–46. https://doi.org/10.56201/ijemt.vol.6.no2.2020.pg19.46

[31] de Jonge B, Scarf PA. A review on maintenance optimization. European Journal of Operational Research. 2020;285(3):805–824. https://doi.org/10.1016/j.ejor.2019.09.047

[32] Van Horenbeek A, Pintelon L, Muchiri P. Maintenance optimization models and criteria. International Journal of System Assurance Engineering and Management. 2010;1(3):189–200. https://doi.org/10.1007/s13198-011-0045-x

[33] Chin HH, Varbanov PS, Klemeš JJ, Benjamin MFD, Tan RR. Asset maintenance optimisation approaches in the chemical and process industries - a review. Chemical Engineering Research and Design. 2020;164:162–194. https://doi.org/10.1016/j.cherd.2020.09.034

[34] Lukens S, Naik M, Saetia K, Hu X. Best practices framework for improving maintenance data quality to enable asset performance analytics. Annual Conference of the PHM Society. 2019;11(1).

[35] Hodkiewicz M, Ho MTW. Cleaning historical maintenance work order data for reliability analysis. Journal of Quality in Maintenance Engineering. 2016;22(2):146. https://doi.org/10.1108/JQME-04-2015-0013

[36] Sexton T, Hodkiewicz M, Brundage MP. Categorization errors for data entry in maintenance work orders. Proceedings of the Annual Conference of the PHM Society. 2019;11.

[37] Wang W. An overview of the recent advances in delay-time-based maintenance modelling. Reliability Engineering & System Safety. 2012;106:165–178. https://doi.org/10.1016/j.ress.2012.04.004

[38] Jiang X, Xu K, Guan X, Qu D, Song X, Zhang Q, et al. A comparative study on the corrosion of gathering pipelines in two sections of a shale gas field. Engineering Failure Analysis. 2021;121:105179. https://doi.org/10.1016/j.engfailanal.2020.105179

[39] Asmara YP, Kurniawan T. Corrosion prediction for corrosion rate of carbon steel in oil and gas environment: A review. Indonesian Journal of Science and Technology. 2018;3(1):64–74. https://doi.org/10.17509/ijost.v3i1.10808

[40] Conte A, Bolland C, Phan L, Brundage M, Sexton T. The impact of data quality on maintenance work order analysis: A case study in HVAC work durations. Proceedings of the 6th European Conference of the Prognostics and Health Management Society 2021. 2021;105–115.

[41] Regattieri A, Manzini R, Battini D. Estimating reliability characteristics in the presence of censored data: A case study in a light commercial vehicle manufacturing system. Reliability Engineering & System Safety. 2010;95(10):1093–1102.

[42] Crespo Márquez A, Moreu de León P, Gómez Fernández JF, Parra Márquez C, López Campos M. The maintenance management framework: A practical view to maintenance management. Journal of Quality in Maintenance Engineering. 2009;15(2):167–178.

[43] Iung B, Levrat E. Advanced maintenance services for promoting sustainability. Procedia CIRP. 2014;22:15–22.

[44] Holgado M, Macchi M, Evans S. Exploring the impacts and contributions of maintenance function for sustainable manufacturing. International Journal of Production Research. 2020;58(23):7292–7310.

[45] CEN. EN 13306:2017 maintenance - maintenance terminology. European Committee for Standardization. 2017.

[46] European Committee for Standardization. Maintenance: Maintenance key performance indicators (EN 15341:2019). CEN. 2019.

[47] European Committee for Standardization. Maintenance: Qualification of maintenance personnel (EN 15628:2014). CEN. 2014.

[48] Ahmad R, Kamaruddin S. An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering. 2012;63(1):135–149.

[49] Mobley RK. An introduction to predictive maintenance (2nd ed.). Butterworth-Heinemann. 2002.

[50] Silva, E. T. da, Piratelli, C. L., & Achcar, J. A. Estudo da manutenção de equipamentos de uma usina de etanol sob a metodologia MCC (manutenção centrada na confiabilidade). Revista Produção Online. 2020;20(4):1331–1353. https://doi.org/10.14488/1676-1901.v20i4.4130

[51] Gideon EN, Adaramola TS, Fadero S. Reliability engineering and failure analysis in complex engineering systems. Iconic Research and Engineering Journals. 2019;2(11):703–727. https://doi.org/10.64388/IREV2I11-1722490

[52] Adaramola TS, Fadero S, Gideon EN. Predictive maintenance and condition monitoring in critical power and energy infrastructure. Iconic Research and Engineering Journals. 2018;2(5):454–477. https://doi.org/10.64388/IREV2I5-1722489

[53] Yu W, et al. A data-driven methodology for the reliability analysis of the natural gas compressor unit considering multiple failure modes. Energies. 2022;15(10):3557. https://doi.org/10.3390/EN15103557

[54] Gupta G, Mishra RP. Identification of critical components using ANP for implementation of reliability centered maintenance. Procedia CIRP. 2018;69:905–909. https://doi.org/10.1016/j.procir.2017.11.122

[55] Bevilacqua M, Braglia M. The analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering & System Safety. 2000;70(1):71–83.

[56] Bertolini M, Bevilacqua M, Bottani E. Multi-attribute approaches for maintenance policies selection problem. Advances in Safety and Reliability: Proceedings of the European Safety and Reliability Conference (ESREL 2005). 2005;1:187–195.

[57] Saaty TL. The analytic hierarchy process. McGraw-Hill. 1980.

[58] Saaty TL. Decision making with the analytic hierarchy process. International Journal of Services Sciences. 2008;1(1):83–98.

[59] Subramanian N, Ramanathan R. A review of applications of analytic hierarchy process in operations management. International Journal of Production Economics. 2012;138(2):215–241.

[60] Ho W, Ma X. The state-of-the-art integrations and applications of the analytic hierarchy process. European Journal of Operational Research. 2018;267(2):399–414.

[61] Shafiee M. Maintenance strategy selection problem: An MCDM overview. Journal of Quality in Maintenance Engineering. 2015;21(4):378–402. https://doi.org/10.1108/JQME-09-2013-0063

[62] Velmurugan RS, Dhingra T. Maintenance strategy selection and its impact in maintenance function: A conceptual framework. International Journal of Operations & Production Management. 2015;35:1622–1661.

[63] Kirubakaran B, Ilangkumaran M. Selection of optimum maintenance strategy based on FAHP integrated with GRA-TOPSIS. Annals of Operations Research. 2016;245:285–313.

[64] Hemmati N, Rahiminezhad Galankashi M, Imani D, Mokhatab Rafiei F. An integrated fuzzy-AHP and TOPSIS approach for maintenance policy selection. International Journal of Quality & Reliability Management. 2020;37:1275–1299.

[65] Jamali N, Feylizadeh MR, Liu P. Prioritization of aircraft maintenance unit strategies using fuzzy analytic network process: A case study. Journal of Air Transport Management. 2021;93:102057.

[66] Emovon I, Norman RA, Murphy AJ. Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing. 2018;29:519–531.

[67] Braglia M, Castellano D, Frosolini M. An integer linear programming approach to maintenance strategies selection. International Journal of Quality & Reliability Management. 2013;30:991–1016.

[68] Faghihinia E, Mollaverdi N. Building a maintenance policy through a multi-criterion decision-making model. Journal of Industrial Engineering International. 2012;8:14.

[69] Özcan EC, Ünlüsoy S, Eren T. A combined goal programming - AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable and Sustainable Energy Reviews. 2017;78:1410–1423.

[70] Ishizaka A, Nemery P. Assigning machines to incomparable maintenance strategies with ELECTRE-SORT. Omega. 2014;47:45–59.

[71] Cheng YH, Tsao HL. Rolling stock maintenance strategy selection, spares parts’ estimation, and replacements’ interval calculation. International Journal of Production Economics. 2010;128(1):404–412.

[72] Carnero MC, Gómez A. Maintenance strategy selection in electric power distribution systems. Energy. 2017;129:255–272.

[73] Shafiee M, et al. Maintenance strategy selection for multi-component systems using a combined analytic network process and cost-risk criticality model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2019;233(2):89–104.

[74] Carpitella S, et al. A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm. Reliability Engineering & System Safety. 2021;205.

[75] Ighravwe DE, Oke SA. Ranking maintenance strategies for sustainable maintenance plan in manufacturing systems using fuzzy axiomatic design principle and fuzzy-TOPSIS. Journal of Manufacturing Technology Management. 2017.

[76] Amrina E, Yulianto A, Kamil I. Fuzzy multi criteria approach for sustainable maintenance evaluation in rubber industry. Procedia Manufacturing. 2019;33:538–545.

[77] Pinjala SK, Pintelon L, Vereecke A. An empirical investigation on the relationship between business and maintenance strategies. International Journal of Production Economics. 2006;104:214–229.

[78] Faccio M, et al. Industrial maintenance policy development: A quantitative framework. International Journal of Production Economics. 2014;147(PA):85–93.

[79] Labib AW. A decision analysis model for maintenance policy selection using a CMMS. Journal of Quality in Maintenance Engineering. 2004;10(3):191–202.

[80] Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing. 2006;20(7):1483–1510. https://doi.org/10.1016/J.YMSSP.2005.09.012

[81] Backlund F, Hannu J. Can we make maintenance decisions on risk analysis results? Journal of Quality in Maintenance Engineering. 2002;8(1):77–91. https://doi.org/10.1108/13552510210420603

[82] Hijes F, Cartagena J. Maintenance strategy based on a multicriterion classification of equipments. Reliability Engineering & System Safety. 2006;91(4):444–451.

[83] Khanlari A, Mohammadi K, Sohrabi B. Prioritizing equipments for preventive maintenance (PM) activities using fuzzy rules. Computers & Industrial Engineering. 2008;54:169–184.

[84] Stadnicka D, Antosz K, Ratnayake RMC. Development of an empirical formula for machine classification: Prioritization of maintenance tasks. Safety Science. 2014;63:34–41.

[85] Jaderi F, Sa’idi E, Anvaripour B, Nabhani N. Criticality analysis for assets priority setting of abadan oil refinery using AHP and delphi techniques. International Journal of Engineering and Innovative Technology. 2012;2(6):48–53.

[86] Singh RK, Kulkarni M. Criticality analysis of power-plant equipments using the analytic hierarchy process. International Journal of Industrial Engineering & Technology. 2013;3(4):1–14.

[87] Jagtap HP, Bewoor AK. Use of analytic hierarchy process methodology for criticality analysis of thermal power plant equipment. Materials Today: Proceedings. 2017;4:1927–1936.

[88] Taghipour S, Banjevic D, Jardine AKS. Prioritization of medical equipment for maintenance decisions. Journal of the Operational Research Society. 2010.

[89] Guo L, Gao J, Yang J, Kang J. Criticality evaluation of petrochemical equipment based on fuzzy comprehensive evaluation and a BP neural network. Journal of Loss Prevention in the Process Industries. 2009;22(4):469–476.

[90] Qi HS, Alzaabi RN, Wood AS, Jani M. A fuzzy criticality assessment system of process equipment for optimised maintenance management. International Journal of Computer Integrated Manufacturing. 2013;28(1):112–125.

[91] Li L, Ni J. Short-term decision support system for maintenance task prioritization. International Journal of Production Economics. 2009;121:195–202.

[92] Liu HC, Liu L, Liu N. Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems with Applications. 2013;40(2):828–838. https://doi.org/10.1016/J.ESWA.2012.08.010

[93] Panchal D, Srivastava P. Qualitative analysis of CNG dispensing system using fuzzy FMEA-GRA integrated approach. International Journal of System Assurance Engineering and Management. 2019;10(1):44–56.

[94] Srivastava A, Khanduja D, Ganesan S. Fuzzy methodology application for risk analysis of mechanical system in process industry. International Journal of System Assurance Engineering and Management. 2020;11(2):297–312.

[95] Roda I, Macchi M, Fumagalli L, Viveros P. A review of multi-criteria classification of spare parts: From literature analysis to industrial evidences. Journal of Manufacturing Technology Management. 2014;25(4):528–549.

[96] Kumar U, Galar D, Parida A, Stenström C, Berges L. Maintenance performance metrics: A state-of-the-art review. Journal of Quality in Maintenance Engineering. 2013.

[97] Parida A, Kumar U, Galar D, Stenström C. Performance measurement and management for maintenance: A literature review. Journal of Quality in Maintenance Engineering. 2015.

[98] Muchiri P, Pintelon L, Gelders L, Martin H. Development of maintenance function performance measurement framework and indicators. International Journal of Production Economics. 2011;131(1):295–302.

[99] Stenström C, Parida A, Kumar U, Galar D. Performance indicators and terminology for value driven maintenance. Journal of Quality in Maintenance Engineering. 2013.

[100] Franciosi C, Roda I, Voisin A, Miranda S, Macchi M, Iung B. Sustainable maintenance performances and EN 15341:2019: An integration proposal. Advances in Production Management Systems (APMS 2021), IFIP Advances in Information and Communication Technology. 2021;401–409.

[101] Franciosi C, Voisin A, Miranda S, Riemma S, Iung B. Measuring maintenance impacts on sustainability of manufacturing industries: From a systematic literature review to a framework proposal. Journal of Cleaner Production. 2020;260:121065.

[102] Franciosi C, Di Pasquale V, Iannone R, Miranda S. Multi-stakeholder perspectives on indicators for sustainable maintenance performance in production contexts: An exploratory study. Journal of Quality in Maintenance Engineering. 2020.

[103] Ibrahim YM, Hami N, Othman SN. Integrating sustainable maintenance into sustainable manufacturing practices and its relationship with sustainability performance: A conceptual framework. International Journal of Energy Economics and Policy. 2019;9(4):30–39.

[104] Jasiulewicz-Kaczmarek M, Żywica P, Gola A. Fuzzy set theory driven maintenance sustainability performance assessment model: A multiple criteria approach. Journal of Intelligent Manufacturing. 2021.

[105] Sari E, Shaharoun AM, Ma’aram A, Yazid AM. Sustainable maintenance performance measures: A pilot survey in malaysian automotive companies. Procedia CIRP. 2015;26:443–448.

[106] Sénéchal O. Research directions for integrating the triple bottom line in maintenance dashboards. Journal of Cleaner Production. 2017;142:331–342.

[107] Olugu EU, Wong KY, Ee JYC, Mammedov YD. Incorporating sustainability and maintenance for performance assessment of offshore oil and gas platforms: A perspective. Sustainability. 2022;14(2):807.

[108] Galante GM, La Fata CM. Combined fuzzy TOPSIS and AHP-based methodology for the prioritization of maintenance key performance indicators: Application to an oil refinery plant. Proceedings of the 2nd International Conference on System Reliability and Safety (ICSRS 2017). 2017;337–342. https://doi.org/10.1109/ICSRS.2017.8272845

[109] Alsyouf I. The role of maintenance in improving companies’ productivity and profitability. International Journal of Production Economics. 2007;105:70–78.

[110] Dowlatshahi S. The role of industrial maintenance in the maquiladora industry: An empirical analysis. International Journal of Production Economics. 2008;114:298–307.

[111] Mostafa S, Lee SH, Dumrak J, Chileshe N, Soltan H. Lean thinking for a maintenance process. Production & Manufacturing Research. 2015;3:236–272.

[112] Okonkwo CS, Agbabiaka J, Ogunwole O, Mayo W, Okeke OT. Conceptual model for materials readiness and maintenance-driven supply chain performance. International Journal of Multidisciplinary Research and Growth Evaluation. 2021;2(6):584–594. https://doi.org/10.54660/.IJMRGE.2021.2.6.584-594

[113] Okonkwo CS, Ogunwole O, Okeke OT. Model for inventory availability and plant uptime improvement in energy facilities. Iconic Research and Engineering Journals. 2018;2(4):160–172. https://doi.org/10.64388/IREV2I4-1713120

[114] Liu XW, Li CY, Dang S, Wang W, Qu J, Chen T, et al. Research on training effectiveness of professional maintenance personnel based on virtual reality and augmented reality technology. Sustainability. 2022;14:14351.

[115] Nakajima S. Introduction to TPM: Total productive maintenance. Productivity Press. 1988.

[116] Ng Corrales, L. d. C., Lambán, M. P., Hernandez Korner, M. E., & Royo, J. Overall equipment effectiveness: Systematic literature review and overview of different approaches. Applied Sciences. 2020;10:6469.

[117] Muchiri P, Pintelon L. Performance measurement using overall equipment effectiveness (OEE): Literature review and practical application discussion. International Journal of Production Research. 2008;46:3517–3535. https://doi.org/10.1080/00207540601142645

[118] Dal B, Tugwell P, Greatbanks R. Overall equipment effectiveness as a measure of operational improvement: A practical analysis. International Journal of Operations & Production Management. 2000;20:1488–1502. https://doi.org/10.1108/01443570010355750

[119] Bamber CJ, Castka P, Sharp JM, Motara Y. Cross-functional team working for overall equipment effectiveness (OEE). Journal of Quality in Maintenance Engineering. 2003;9:223–238. https://doi.org/10.1108/13552510310493684

[120] Braglia M, Frosolini M, Zammori F. Overall equipment effectiveness of a manufacturing line (OEEML). Journal of Manufacturing Technology Management. 2008;20:8–29. https://doi.org/10.1108/17410380910925389

[121] Garza-Reyes JA. From measuring overall equipment effectiveness (OEE) to overall resource effectiveness (ORE). Journal of Quality in Maintenance Engineering. 2015;21:506–527. https://doi.org/10.1108/JQME-03-2014-0014

[122] Jonsson P, Lesshammar M. Evaluation and improvement of manufacturing performance measurement systems: The role of OEE. International Journal of Operations & Production Management. 1999;19:55–78. https://doi.org/10.1108/01443579910244223

[123] Gupta AK, Garg R. OEE improvement by TPM implementation: A case study. International Journal of IT, Engineering and Applied Sciences Research. 2012;1:115–124.

[124] Ylipää T, Skoogh A, Bokrantz J, Gopalakrishnan M. Identification of maintenance improvement potential using OEE assessment. International Journal of Productivity and Performance Management. 2017;66:126–143.

[125] Mansour H, Ahmad MM, Dhafr N, Ahmed H. Evaluation of operational performance of workover rigs activities in oilfields. International Journal of Productivity and Performance Management. 2013;62:204–218. https://doi.org/10.1108/17410401311295740

[126] Palanisamy V. Implementing overall equipment effectiveness in a process industry. Indian Journal of Science and Technology. 2013;6:1–5. https://doi.org/10.17485/ijst/2013/v6isp6.17

[127] Fekri Sari M, Avakh Darestani S. Fuzzy overall equipment effectiveness and line performance measurement using artificial neural network. Journal of Quality in Maintenance Engineering. 2019;25:340–354. https://doi.org/10.1108/JQME-12-2017-0085

[128] Zennaro I, Battini D, Sgarbossa F, Persona A, De Marchi R. Micro downtime: Data collection, analysis and impact on OEE in bottling lines the san benedetto case study. International Journal of Quality & Reliability Management. 2018;35:965–995. https://doi.org/10.1108/IJQRM-11-2016-0202

[129] Sharma R. Overall equipment effectiveness measurement of TPM manager model machines in flexible manufacturing environment: A case study of automobile sector. International Journal of Productivity and Quality Management. 2019;26:206–222. https://doi.org/10.1504/IJPQM.2019.097767

[130] Singh K, Ahuja IPS. Assessing the business performance measurements for transfusion of TQM and TPM initiatives in the indian manufacturing industry. International Journal of Technology, Policy and Management. 2014;14:44. https://doi.org/10.1504/IJTPM.2014.058733

[131] Benjamin SJ, Marathamuthu MS, Murugaiah U. The use of 5-WHYs technique to eliminate OEE’s speed loss in a manufacturing firm. Journal of Quality in Maintenance Engineering. 2015;21:419–435. https://doi.org/10.1108/JQME-09-2013-0062

[132] Samatemba B, Zhang L, Besa B. Evaluating and optimizing the effectiveness of mining equipment; the case of chibuluma south underground mine. Journal of Cleaner Production. 2020;252:119697. https://doi.org/10.1016/j.jclepro.2019.119697

[133] Rastegari A, Mobin M. Maintenance decision making, supported by computerized maintenance management system. 2016 Annual Reliability and Maintainability Symposium (RAMS). 2016;1–8.

[134] Fernandez O, Labib AW, Walmsley R, Petty DJ. A decision support maintenance management system: Development and implementation. International Journal of Quality & Reliability Management. 2003;20(8):965–979.

[135] Brundage MP, Sexton T, Hodkiewicz M, Dima A, Lukens S. Technical language processing: Unlocking maintenance knowledge. Manufacturing Letters. 2021;27:42–46. https://doi.org/10.1016/j.mfglet.2020.11.001

[136] Sexton TB, Brundage MP. Nestor: A tool for natural language annotation of short texts. Journal of Research of the National Institute of Standards and Technology. 2019;124.

[137] Sexton T, Brundage MP, Hoffman M, Morris KC. Hybrid datafication of maintenance logs from AI-assisted human tags. 2017 IEEE International Conference on Big Data (Big Data). 2017;1769–1777. https://doi.org/10.1109/BigData.2017.8258120

[138] Brundage MP, Morris KC, Sexton T, Moccozet S, Hoffman M. Developing maintenance key performance indicators from maintenance work order data. International Manufacturing Science and Engineering Conference (ASME). 2018;51371:V003T02A027.

[139] Navinchandran M, Sharp ME, Brundage MP, Sexton TB. Discovering critical KPI factors from natural language in maintenance work orders. Journal of Intelligent Manufacturing. 2021.

[140] Wang TY, Pan HC. Improving the OEE and UPH data quality by automated data collection for the semiconductor assembly industry. Expert Systems with Applications. 2011;38:5764–5773. https://doi.org/10.1016/j.eswa.2010.10.056

[141] Adaramola TS, Gideon EN, Fadero S. Machine learning and data-driven methods for industrial automation and process control. International Journal of Engineering and Modern Technology. 2020;6(3):103–128. https://doi.org/10.56201/ijemt.vol.6.no3.2020.pg103.128

[142] Komi NM, Adamolekun A. Interpretable machine learning for early failure prediction in distributed renewable energy assets. International Journal of Multidisciplinary Research and Growth Evaluation. 2021;2(6):1015–1038. https://doi.org/10.54660/.IJMRGE.2021.2.6.1015-1038

[143] Amaechi CV, Reda A, Kgosiemang IM, Ja’e IA, Oyetunji AK, Olukolajo MA, et al. Guidelines on asset management of offshore facilities for monitoring, sustainable maintenance, and safety practices. Sensors. 2022;22(19):7270. https://doi.org/10.3390/s22197270

[144] Zhang Y, Pang X, Qu S, et al. Discussion of the CO2 corrosion mechanism between low partial pressure and supercritical condition. Corrosion Science. 2012;59:186–197.

[145] Hua Y, Barker R, Charpentier T, et al. Relating iron carbonate morphology to corrosion characteristics for water-saturated supercritical CO2 systems. Journal of Supercritical Fluids. 2015;98:183–193.

[146] Sun C, Sun J, Wang Y, et al. Synergistic effect of O2, H2S and SO2 impurities on the corrosion behavior of X65 steel in water-saturated supercritical CO2 system. Corrosion Science. 2016;107:193–203.

[147] Choi YS, Nesic S, Young D. Effect of impurities on the corrosion behavior of CO2 transmission pipeline steel in supercritical CO2-water environments. Environmental Science & Technology. 2010;44(23):9233–9238.

[148] Xiang Y, Xu M, Choi YS. State-of-the-art overview of pipeline steel corrosion in impure dense CO2 for CCS transportation: Mechanisms and models. Corrosion Engineering, Science and Technology. 2017;52(7):485–509. https://doi.org/10.1080/1478422X.2017.1304690

[149] Cole IS, Corrigan P, Sim S, et al. Corrosion of pipelines used for CO2 transport in CCS: Is it a real problem? International Journal of Greenhouse Gas Control. 2011;5(4):749–756.

[150] Li Q, Hu H, Cheng YF. Corrosion of pipelines in CO2 saturated oil-water emulsion flow studied by electrochemical measurements and computational fluid dynamics modeling. Journal of Petroleum Science and Engineering. 2016;147:408–415. https://doi.org/10.1016/j.petrol.2016.09.011

[151] Sim S, Cole IS, Choi YS, et al. A review of the protection strategies against internal corrosion for the safe transport of supercritical CO2 via steel pipelines for CCS purposes. International Journal of Greenhouse Gas Control. 2014;29:185–199.

[152] Alamri AH. Localized corrosion and mitigation approach of steel materials used in oil and gas pipelines: An overview. Engineering Failure Analysis. 2020;116:104735. https://doi.org/10.1016/j.engfailanal.2020.104735

[153] Ilman, M. N., & Kusmono. Analysis of internal corrosion in subsea oil pipeline. Case Studies in Engineering Failure Analysis. 2014;2:1–8.

[154] Kakooei S, Ismail MC, Ariwahjoedi B. Mechanisms of microbiologically influenced corrosion: A review. World Applied Sciences Journal. 2012;17(4):524–531.

[155] Spark A, Wang K, Cole I, Law D, Ward L. Microbiologically influenced corrosion: A review of the studies conducted on buried pipelines. Corrosion Reviews. 2020;38(3):231–262.

[156] Obetta PC, Ninduwezuor-Ehiobu N, Olisakwe HC. Corrosion management and material selection for crude oil export equipment in marine environments: A review. International Journal of Multidisciplinary Futuristic Development. 2020;1(1):92–110. https://doi.org/10.54660/IJMFD.2020.1.1.92-110

[157] Qiao Q, Cheng G, Li Y, Wu W, Hu H, Huang H. Corrosion failure analyses of an elbow and an elbow-to-pipe weld in a natural gas gathering pipeline. Engineering Failure Analysis. 2017;82:599–616. https://doi.org/10.1016/j.engfailanal.2017.04.016

[158] Islam MA, Farhat ZN. The synergistic effect between erosion and corrosion of API pipeline in CO2 and saline medium. Tribology International. 2013;68:26–34. https://doi.org/10.1016/j.triboint.2012.10.026

[159] Chen Y, Wu H, Chen Y, Li P, Wang Q. Erosion-corrosion coupling analysis of shale gas production pipe. Engineering Failure Analysis. 2022;138:106308. https://doi.org/10.1016/j.engfailanal.2022.106308

[160] Jones M, Llewellyn RJ. Erosion-corrosion assessment of materials for use in the resources industry. Wear. 2009;267:2003–2009. https://doi.org/10.1016/j.wear.2009.06.025

[161] Tang X, Xu LY, Cheng YF. Electrochemical corrosion behavior of x-65 steel in the simulated oil-sand slurry. II: Synergism of erosion and corrosion. Corrosion Science. 2008;50:1469–1474. https://doi.org/10.1016/j.corsci.2008.01.019

[162] Lu BT, Luo JL, Guo HX, Mao LC. Erosion-enhanced corrosion of carbon steel at passive state. Corrosion Science. 2011;53:432–440. https://doi.org/10.1016/j.corsci.2010.09.054

[163] Jia W, Zhang Y, Li C, Luo P, Song X, Wang Y, et al. Experimental and numerical simulation of erosion-corrosion of 90° steel elbow in shale gas pipeline. Journal of Natural Gas Science and Engineering. 2021;89:103871. https://doi.org/10.1016/j.jngse.2021.103871

[164] El-Sayed MH. Flow enhanced corrosion of water injection pipelines. Engineering Failure Analysis. 2015;50:1–6. https://doi.org/10.1016/j.engfailanal.2015.01.007

[165] Khan R, Ya HH, Pao W, Majid MAA, Ahmed T, Ahmad A, et al. Effect of sand fines concentration on the erosion-corrosion mechanism of carbon steel 90° elbow pipe in slug flow. Materials. 2020;13:4601. https://doi.org/10.3390/ma13204601

[166] Thaker J, Banerjee J. Influence of intermittent flow sub-patterns on erosion-corrosion in horizontal pipe. Journal of Petroleum Science and Engineering. 2016;145:298–320. https://doi.org/10.1016/j.petrol.2016.05.006

[167] Wongpanya P, Saramas Y, Chumkratoke C, Wannakomol A. Erosion-corrosion behaviors of 1045 and J55 steels in crude oil. Journal of Petroleum Science and Engineering. 2020;189:106965. https://doi.org/10.1016/j.petrol.2020.106965

[168] Stack MM, Abdulrahman GH. Mapping erosion-corrosion of carbon steel in oil-water solutions: Effects of velocity and applied potential. Wear. 2012;274–275:401–413. https://doi.org/10.1016/j.wear.2011.10.008

[169] Xu Y, Liu L, Zhou Q, Wang X, Tan MY, Huang Y. An overview of major experimental methods and apparatus for measuring and investigating erosion-corrosion of ferrous-based steels. Metals. 2020;10:180. https://doi.org/10.3390/met10020180

[170] Adewoyin MA, Ogunnowo EO, Fiemotongha JE, Igunma TO, Adeleke AK. Advances in CFD-driven design for fluid-particle separation and filtration systems in engineering applications. Iconic Research and Engineering Journals. 2021;5(3):347–365.

[171] Cao Q, Pojtanabuntoeng T, Esmaily M, Thomas S, Brameld M, Amer A, et al. A review of corrosion under insulation: A critical issue in the oil and gas industry. Metals. 2022;12(4):561. https://doi.org/10.3390/met12040561

[172] Javaherdashti R. Corrosion under insulation (CUI): A review of essential knowledge and practice. Journal of Materials Science & Surface Engineering. 2014;1:36–43.

[173] Winnik S. Corrosion under insulation (CUI) guidelines (2nd ed.). Elsevier (European Federation of Corrosion). 2015.

[174] NACE International. NACE SP0198–2017: Control of corrosion under thermal insulation and fireproofing materials: A systems approach. NACE International. 2017.

[175] American Petroleum Institute. API RP 583: Corrosion under insulation and fireproofing (2nd ed.). American Petroleum Institute. 2021.

[176] Eltai EO, Musharavati F, Mahdi E. Severity of corrosion under insulation (CUI) to structures and strategies to detect it. Corrosion Reviews. 2019. https://doi.org/10.1515/corrrev-2018-0102

[177] Caines S, Khan F, Shirokoff J, Qiu W. Experimental design to study corrosion under insulation in harsh marine environments. Journal of Loss Prevention in the Process Industries. 2015;33:39–51. https://doi.org/10.1016/j.jlp.2014.10.014

[178] Geary W. Analysis of a corrosion under insulation failure in a carbon steel refinery hydrocarbon line. Case Studies in Engineering Failure Analysis. 2013;1:249–256. https://doi.org/10.1016/j.csefa.2013.09.001

[179] Eltai E, Alkhalifa K, Al-Rayashi A, Mahdi E, Hamouda AMS. Investigating the corrosion under insulation (CUI) on steel pipe exposed to Arabian Gulf sea water drops. Key Engineering Materials. 2016;689:148–153. https://doi.org/10.4028/www.scientific.net/KEM.689.148

[180] Cao Q, Esmaily M, Liu RL, Birbilis N, Thomas S. Corrosion of mild steel under insulation: The effect of dissolved metal ions. Corrosion Engineering, Science and Technology. 2020;55:322–330. https://doi.org/10.1080/1478422X.2020.1734737

[181] Pojtanabuntoeng T, Machuca LL, Salasi M, Kinsella B, Cooper M. Influence of drain holes in jacketing on corrosion under thermal insulation. Corrosion. 2015;71:1511–1520. https://doi.org/10.5006/1861

[182] Mohsin KM, Mokhtar AA, Tse PW. A fuzzy logic method: Predicting corrosion under insulation of piping systems with modelling of CUI 3D surfaces. International Journal of Pressure Vessels and Piping. 2019;175:103929. https://doi.org/10.1016/j.ijpvp.2019.103929

[183] Pechacek RW. NDT inspection of insulated vessels and piping for interior corrosion and corrosion under insulation. Materials Performance. 2004;43:28–32.

[184] Mokhtar AA, Che Ismail M, Zainordin AB, Shahid S. A framework for estimating piping reliability subject to corrosion under insulation. MATEC Web of Conferences (ICPER 2014). 2014;13:03001.

[185] Ahmadi O, Mortazavi SB, Mahabadi HA. Review of atmospheric storage tank fire scenarios: Costs and causes. Journal of Failure Analysis and Prevention. 2020;20:384–405. https://doi.org/10.1007/s11668-020-00846-5

[186] Moshashaei P, Alizadeh SS, Khazini L, Asghari-Jafarabadi M. Investigate the causes of fires and explosions at external floating roof tanks: A comprehensive literature review. Journal of Failure Analysis and Prevention. 2017;17(5):1044–1052. https://doi.org/10.1007/s11668-017-0333-0

[187] Mishra KB, Wehrstedt KD, Krebs H. Lessons learned from recent fuel storage fires. Fuel Processing Technology. 2013;107:166–172. https://doi.org/10.1016/j.fuproc.2012.08.003

[188] Zhou Y, Zhao X, Zhao J, Chen D. Research on fire and explosion accidents of oil depots. Chemical Engineering Transactions. 2016;51:163–168. https://doi.org/10.3303/CET1651028

[189] Cornell JR, Baker MA. Catastrophic tank failures: Highlights of past failures along with proactive tank designs. Proceedings of the US EPA Fourth Biennial Freshwater Spills Symposium, Cleveland, Ohio. 2002.

[190] Nivolianitou Z, Argyropoulos C, Christolis M, Markatos N. A methodology for the hazard assessment in large hydrocarbon fuel tanks. Chemical Engineering Transactions. 2012;26:171–176.

[191] Crippa C, Fiorentini L, Rossini V, Stefanelli R, Tafaro S, Marchi M. Fire risk management system for safe operation of large atmospheric storage tanks. Journal of Loss Prevention in the Process Industries. 2009;22(5):574–578.

[192] Riezel Y. Explosion and fire in a gas-oil fixed roof storage tank: Case study and lessons learned. Process Safety Progress. 2002;21(1):67–73.

[193] Godoy LA, Batista-Abreu JC. Buckling of fixed-roof aboveground oil storage tanks under heat induced by an external fire. Thin-Walled Structures. 2012;52:90–101.

[194] American Petroleum Institute. API standard 650: Welded steel tanks for oil storage. American Petroleum Institute. 2007.

[195] Dey PK. A risk-based model for inspection and maintenance of cross-country petroleum pipeline. Journal of Quality in Maintenance Engineering. 2001;7:25–43. https://doi.org/10.1108/13552510110386874

[196] Gomes WJS, Beck AT, Haukaas T. Optimal inspection planning for onshore pipelines subject to external corrosion. Reliability Engineering & System Safety. 2013;118:18–27. https://doi.org/10.1016/j.ress.2013.04.011

[197] Alamilla JL, Oliveros J, García-Vargas J. Probabilistic modelling of a corroded pressurized pipeline at inspection time. Structure and Infrastructure Engineering(2), 91–104; 2009.

[198] Schoefs F, Boéro J. The αδ method for modeling NDT results in risk based inspection of corroded steel wharves. Matériaux & Techniques. 2013;101(5–6):507. https://doi.org/10.1051/mattech/2013081

[199] Soomro AA, Mokhtar AA, Kurnia JC, Lashari N, Sarwar U, Jameel SM, et al. A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion. International Journal of Pressure Vessels and Piping. 2022;200:104841. https://doi.org/10.1016/j.ijpvp.2022.104841

[200] Shahriar A, Sadiq R, Tesfamariam S. Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis. Journal of Loss Prevention in the Process Industries. 2012;25:505–523.

[201] Zakikhani K, Nasiri F, Zayed T. A review of failure prediction models for oil and gas pipelines. Journal of Pipeline Systems Engineering and Practice. 2020;11:03119001. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000407

[202] Ahmed SK, Kabir G. An integrated approach for failure analysis of natural gas transmission pipeline. CivilEng. 2021;2:87–119.

[203] Vanaei HR, Eslami A, Egbewande A. A review on pipeline corrosion, in-line inspection (ILI), and corrosion growth rate models. International Journal of Pressure Vessels and Piping. 2017;149:43–54. https://doi.org/10.1016/j.ijpvp.2016.11.007

[204] Ma Q, Tian G, Zeng Y, Li R, Song H, Wang Z, et al. Pipeline in-line inspection method, instrumentation and data management. Sensors. 2021;21(11):3862. https://doi.org/10.3390/s21113862

[205] Coramik M, Ege Y. Discontinuity inspection in pipelines: A comparison review. Measurement. 2017;111:359–373. https://doi.org/10.1016/j.measurement.2017.07.058

[206] Budhe S, Banea MD, Rohem NRF, Sampaio EM, de Barros S. Failure pressure analysis of composite repair system for wall loss defect of metallic pipelines. Composite Structures. 2017;176:1013–1019.

[207] Ogunnowo EO, Adewoyin MA, Fiemotongha JE, Igunma TO, Adeleke AK. Systematic review of non-destructive testing methods for predictive failure analysis in mechanical systems. Iconic Research and Engineering Journals. 2020;4(4):207–222.

[208] Obetta PC, Ninduwezuor-Ehiobu N, Olisakwe HC. Advances in non-intrusive inspection of pressure vessels: Reducing shutdown frequency and OPEX in oil and gas operations. International Journal of Multidisciplinary Research and Growth Evaluation. 2021;2(6):1137–1153. https://doi.org/10.54660/.IJMRGE.2021.2.6.1137-1153

[209] Atta S, Tofah P, Adenuga OM. Advances in non-destructive testing methods for weld integrity evaluation in high-capacity power infrastructure. International Journal of Engineering and Modern Technology. 2021;7(1):20–47. https://doi.org/10.56201/ijemt.vol.7.no1.2021.pg20.47

[210] Atta S, Adjaklo E, Asomani E, Adenuga OM. Advances in welding inspection standards and quality assurance practices in large-scale power generation projects. International Journal of Engineering and Modern Technology. 2022;8(5):101–124. https://doi.org/10.56201/ijemt.v8.no5.2022.pg101.124

[211] Dagodzo D, Ahiaeke Patrick MC. A review of GIS applications in utility asset management and infrastructure planning. Iconic Research and Engineering Journals. 2021;5(3):468–492. https://doi.org/10.64388/IREV5I3-1716085

[212] Dagodzo D, Ahiaeke Patrick MC. UAV-based pipeline and corridor monitoring: A review of current practices and emerging technologies. Iconic Research and Engineering Journals. 2020;3(10):574–597. https://doi.org/10.64388/IREV3I10-1716084

[213] Anderson TL, Osage DA. API 579: A comprehensive fitness-for-service guide. International Journal of Pressure Vessels and Piping. 2000;77:953–963.

[214] Osage DA. Fatigue assessment for in-service components: A new part for API 579–1/ASME FFS-1 fitness-for-service. Procedia Engineering. 2015;133:320–347.

[215] Cravero S, Ruggieri C. Structural integrity analysis of axially cracked pipelines using conventional and constraint-modified failure assessment diagrams. International Journal of Pressure Vessels and Piping. 2006;83:607–617.

[216] Pluvinage G, Bouledroua O, Hadj Meliani M, Suleiman R. Corrosion defect analysis using domain failure assessment diagram. International Journal of Pressure Vessels and Piping. 2018;165:126–134.

[217] Muthanna BGN, Bouledroua O, Meriem-Benziane M, Setvati MR, Djukic MB. Assessment of corroded API 5L X52 pipe elbow using a modified failure assessment diagram. International Journal of Pressure Vessels and Piping. 2021;190:104291.

[218] Ainsworth RA, Gintalas M, Sahu MK, Chattopadhyay J, Dutta BK. Application of failure assessment diagram methods to cracked straight pipes and elbows. International Journal of Pressure Vessels and Piping. 2016;148:26–35.

[219] Ghanbari M, Ranjbar Naserabadi MJ, Mirzadeh F. Evaluation of a pressure vessel using failure analysis diagram based on phased array ultrasonic testing data. Journal of Structural Integrity and Maintenance. 2022;7(3):198–205.

[220] British Standards Institution. BS 7910:2013+A1:2015 guide to methods for assessing the acceptability of flaws in metallic structures. BSI. 2015.

[221] American Society of Mechanical Engineers. ASME B31G-2009: Manual for determining the remaining strength of corroded pipelines. ASME. 2009.

[222] Ma B, Shuai J, Wang J, Han K. Analysis on the latest assessment criteria of ASME B31G-2009 for the remaining strength of corroded pipelines. Journal of Failure Analysis and Prevention. 2011;11:666–667.

[223] Det Norske Veritas. DNV-RP-F101: Corroded pipelines. DNV. 2015.

[224] Canadian Standards Association. CSA Z662–19: Oil and gas pipeline systems. CSA Group. 2019.

[225] Cosham A, Hopkins P, Macdonald KA. Best practice for the assessment of defects in pipelines. Engineering Failure Analysis. 2007;14:1245–1265.

[226] Zhu XK, Leis BN. Evaluation of burst pressure prediction models for line pipes. International Journal of Pressure Vessels and Piping. 2012;89:85–97.

[227] Zhu XK. A comparative study of burst failure models for assessing remaining strength of corroded pipelines. Journal of Pipeline Science and Engineering. 2021;1(1):36–50.

[228] Zhou W, Huang GX. Model error assessments of burst capacity models for corroded pipelines. International Journal of Pressure Vessels and Piping. 2012;99–100:1–8.

[229] Bhardwaj U, Teixeira AP, Guedes Soares C. Uncertainty quantification of burst pressure models of corroded pipelines. International Journal of Pressure Vessels and Piping. 2020;188:104208.

[230] Chauhan V, Brister J. A review of methods for assessing the remaining strength of corroded pipelines (final report 153A). U.S. Department of Transportation. 2009.

[231] Choi JB, Goo BK, Kim JC, Kim Y, Kim W. Development of limit load solutions for corroded gas pipelines. International Journal of Pressure Vessels and Piping. 2003;80:121–128.

[232] Yeom KJ, Lee YK, Oh KH, Kim WS. Integrity assessment of a corroded API X70 pipe with a single defect by burst pressure analysis. Engineering Failure Analysis. 2015;57:553–561.

[233] Chiodo MSG, Ruggieri C. Failure assessments of corroded pipelines with axial defects using stress-based criteria: Numerical studies and verification analyses. International Journal of Pressure Vessels and Piping. 2009;86:164–176.

[234] Velázquez JC, González-Arévalo NE, Díaz-Cruz M, Cervantes-Tobón A, Herrera-Hernández H, Hernández-Sánchez E. Failure pressure estimation for an aged and corroded oil and gas pipeline: A finite element study. Journal of Natural Gas Science and Engineering. 2022;101:104532.

[235] Sun J, Cheng YF. Assessment by finite element modeling of the interaction of multiple corrosion defects and the effect on failure pressure of corroded pipelines. Engineering Structures. 2018;165:278–286.

[236] Bony M, Alamilla JL, Vai R, Flores E. Failure pressure in corroded pipelines based on equivalent solutions for undamaged pipe. Journal of Pressure Vessel Technology. 2010;132:051001.

[237] Ma B, Shuai J, Liu D, Xu K. Assessment on failure pressure of high strength pipeline with corrosion defects. Engineering Failure Analysis. 2013;32:209–219.

[238] Idris NN, Mustaffa Z, Ben Seghier MEA, Trung NT. Burst capacity and development of interaction rules for pipelines considering radial interacting corrosion defects. Engineering Failure Analysis. 2021;121:105124.

[239] Oh DH, Race J, Oterkus S, Chang E. A new methodology for the prediction of burst pressure for API 5L x grade flawless pipelines. Ocean Engineering. 2020;212:107602.

[240] Bhardwaj U, Teixeira AP, Guedes Soares C. Burst strength assessment of X100 to X120 ultra-high strength corroded pipes. Ocean Engineering. 2021;241:110004.

[241] Lyons CJ, Race JM, Chang E, Cosham A, Wetenhall B, Barnett J. Validation of the NG-18 equations for thick-walled pipelines. Engineering Failure Analysis. 2020;112:104494.

[242] Leis BN. Evolution of metal-loss severity criteria: Gaps and forward. Journal of Pipeline Science and Engineering. 2021;1:51–62.

[243] Li H, Huang K, Zeng Q, Sun C. Residual strength assessment and residual life prediction of corroded pipelines: A decade review. Energies. 2022;15:726.

[244] Amaya-Gómez R, Sánchez-Silva M, Bastidas-Arteaga E, Schoefs F, Muñoz F. Reliability assessments of corroded pipelines based on internal pressure: A review. Engineering Failure Analysis. 2019;98:190–214.

[245] Ben Seghier MEA, Keshtegar B, Elahmoune B. Reliability analysis of low, mid and high-grade strength corroded pipes based on plastic flow theory using adaptive nonlinear conjugate map. Engineering Failure Analysis. 2018;90:245–261.

[246] Cai J, Jiang X, Yang Y, Lodewijks G, Wang M. Data-driven methods to predict the burst strength of corroded pipelines subjected to internal pressure. Journal of Marine Science and Application. 2022;21:115–132.

[247] Zhu XK, Johnson WR, Sindelar R, Wiersma B. Artificial neural network models of burst strength for thin-wall pipelines. Journal of Pipeline Science and Engineering. 2022;2:100090.

[248] Chin KT, Arumugam T, Karuppanan S, Ovinis M. Failure pressure prediction of pipeline with single corrosion defect using artificial neural network. Pipeline Science and Technology. 2020;4:10–17.

[249] Ossai CI. Corrosion defect modelling of aged pipelines with a feed-forward multi-layer neural network for leak and burst failure estimation. Engineering Failure Analysis. 2020;110:104397.

[250] Lo M, Karuppanan S, Ovinis M. ANN- and FEA-based assessment equation for a corroded pipeline with a single corrosion defect. Journal of Marine Science and Engineering. 2022;10:476.

[251] Das AN, Sarmah SP. Preventive replacement models: An overview and their application in process industries. European Journal of Industrial Engineering. 2010;4(3):280–307.

[252] Thomas LC. A survey of maintenance and replacement models for maintainability and reliability of multi-item systems. Reliability Engineering. 1986;16(4):297–309. https://doi.org/10.1016/0143-8174(86)90099-5

[253] Cho DI, Parlar M. A survey of maintenance models for multi-unit systems. European Journal of Operational Research. 1991;51(1):1–23.

[254] Nicolai RP, Dekker R. Optimal maintenance of multi-component systems: A review. In K. A. H. Kobbacy & D. N. P. Murthy (Eds.), Complex System Maintenance Handbook (pp. 263–286). Springer. 2008;263–286. https://doi.org/10.1007/978-1-84800-011-7_11

[255] Nowakowski T, Werbińska-Wojciechowska S. On problems of multicomponent system maintenance modelling. International Journal of Automation and Computing. 2009;6(4):364–378. https://doi.org/10.1007/s11633-009-0364-4

[256] Nakagawa T. Maintenance theory of reliability. Springer. 2005.

[257] Nakagawa T. A summary of discrete replacement policies. European Journal of Operational Research. 1984;17(3):382–392.

[258] Nakagawa T. Random maintenance policies. Springer. 2014.

[259] Zhao X, Nakagawa T. Optimization problems of replacement first or last in reliability theory. European Journal of Operational Research. 2012;223(1):141–149.

[260] Zhao X, Mizutani S, Nakagawa T. Which is better for replacement policies with continuous or discrete scheduled times? European Journal of Operational Research. 2015;242(2):477–486.

[261] Mizutani S, Zhao X, Nakagawa T. Age and periodic replacement policies with two failure modes in general replacement models. Reliability Engineering & System Safety. 2021;214. https://doi.org/10.1016/j.ress.2021.107754

[262] Badía FG, Berrade MD, Lee H. An study of cost effective maintenance policies: Age replacement versus replacement after n minimal repairs. Reliability Engineering & System Safety. 2020;201.

[263] Eryilmaz S. Revisiting discrete time age replacement policy for phase-type lifetime distributions. European Journal of Operational Research. 2021;295(2):699–704.

[264] Eryilmaz S. Age-based preventive maintenance for coherent systems with applications to consecutive-k-out-of-n and related systems. Reliability Engineering & System Safety. 2020;204.

[265] Safaei F, Châtelet E, Ahmadi J. Optimal age replacement policy for parallel and series systems with dependent components. Reliability Engineering & System Safety. 2020;197.

[266] Hamidi M, Szidarovszky F, Szidarovszky M. New one cycle criteria for optimizing preventive replacement policies. Reliability Engineering & System Safety. 2016;154:42–48.

[267] Sheu SH, Liu TH, Zhang ZG. Extended optimal preventive replacement policies with random working cycle. Reliability Engineering & System Safety. 2019;188:398–415.

[268] Yang L, Zhao Y, Peng R, Ma X. Hybrid preventive maintenance of competing failures under random environment. Reliability Engineering & System Safety. 2018;174:130–140. https://doi.org/10.1016/j.ress.2018.02.017

[269] Shafiee M, Finkelstein M. An optimal age-based group maintenance policy for multi-unit degrading systems. Reliability Engineering & System Safety. 2015;134:230–238.

[270] Christer AH. Developments in delay time analysis for modelling plant maintenance. Journal of the Operational Research Society. 1999;50(11):1120–1137.

[271] Christer AH, Lee C. Refining the delay-time-based PM inspection model with non-negligible system downtime estimates of the expected number of failures. International Journal of Production Economics. 2000;67(1):77–85.

[272] Christer AH, Redmond DF. Revising models of maintenance and inspection. International Journal of Production Economics. 1992;24(3):227–234.

[273] Baker RD, Christer AH. Review of delay-time OR modelling of engineering aspects of maintenance. European Journal of Operational Research. 1994;73(3):407–422.

[274] Wang W. Delay time modelling. In K. A. H. Kobbacy & D. N. P. Murthy (Eds.), Complex System Maintenance Handbook (pp. 345–370). Springer. 2008;345–370.

[275] Christer AH, Lee C, Wang W. A data deficiency based parameter estimating problem and case study in delay time PM modeling. International Journal of Production Economics. 2000;67(1):63–76.

[276] Wang W. Subjective estimation of the delay time distribution in maintenance modelling. European Journal of Operational Research. 1997;99(3):516–529.

[277] Jones B, Jenkinson I, Wang J. Methodology of using delay-time analysis for a manufacturing industry. Reliability Engineering & System Safety. 2009;94(1):111–124.

[278] Aven T, Castro IT. A delay-time model with safety constraint. Reliability Engineering & System Safety. 2009;94(2):261–267.

[279] Wang W. An inspection model for a process with two types of inspections and repairs. Reliability Engineering & System Safety. 2009;94(2):526–533.

[280] Wang W. An inspection model based on a three-stage failure process. Reliability Engineering & System Safety. 2011;96(7):838–848.

[281] Wang W, Banjevic D, Pecht M. A multi-component and multi-failure mode inspection model based on the delay time concept. Reliability Engineering & System Safety. 2010;95(8):912–920.

[282] Wang W, Banjevic D. Ergodicity of forward times of the renewal process in a block-based inspection model using the delay time concept. Reliability Engineering & System Safety. 2012;100:1–7.

[283] Berrade MD, Scarf PA, Cavalcante CAV. A study of postponed replacement in a delay time model. Reliability Engineering & System Safety. 2017;168:70–79.

[284] Cavalcante CAV, Scarf PA, de Almeida AT. A study of a two-phase inspection policy for a preparedness system with a defective state and heterogeneous lifetime. Reliability Engineering & System Safety. 2011;96(6):627–635.

[285] Cerone P. Inspection interval for maximum future reliability using the delay time model. European Journal of Operational Research. 1993;68(2):236–250.

[286] Redmond DF, Christer AH, Rigden SR, Burley E, Tajelli A, Abu-Tair A. O.R. modelling of the deterioration and maintenance of concrete structures. European Journal of Operational Research. 1997;99(3):619–631.

[287] Luss H. An inspection policy model for production facilities. Management Science. 1983;29(9):1102–1109.

[288] Cavalcante CAV, Lopes RS, Scarf PA. Inspection and replacement policy with a fixed periodic schedule. Reliability Engineering & System Safety. 2021;208.

[289] Apeland S, Scarf PA. A fully subjective approach to modelling inspection maintenance. European Journal of Operational Research. 2003;148(2):410–425.

[290] Ferreira RJP, de Almeida AT, Cavalcante CAV. A multi-criteria decision model to determine inspection intervals of condition monitoring based on delay time analysis. Reliability Engineering & System Safety. 2009;94(5):905–912.

[291] Wang W. A joint spare part and maintenance inspection optimisation model using the delay-time concept. Reliability Engineering & System Safety. 2011;96(11):1535–1541.

[292] Wang W. A stochastic model for joint spare parts inventory and planned maintenance optimisation. European Journal of Operational Research. 2012;216(1):127–139.

[293] Dekker R, Wildeman RE, van der Duyn Schouten FA. A review of multi-component maintenance models with economic dependence. Mathematical Methods of Operations Research. 1997;45(3):411–435. https://doi.org/10.1007/BF01194788

[294] Wildeman RE, Dekker R. Dynamic influences in multi-component maintenance. Quality and Reliability Engineering International. 1997;13(4):199–207.

[295] Van der Duyn Schouten FA, Vanneste SG. Two simple control policies for a multicomponent maintenance system. Operations Research. 1993;41(6):1125–1136.

[296] Sheu SH, Jhang JP. A generalized group maintenance policy. European Journal of Operational Research. 1996;96(2):232–247.

[297] Radner R, Jorgenson DW. Opportunistic replacement of a single part in the presence of several monitored parts. Management Science. 1963;10(1):70–84.

[298] Ab-Samat H, Kamaruddin S. Opportunistic maintenance (OM) as a new advancement in maintenance approaches: A review. Journal of Quality in Maintenance Engineering. 2014;20(2):98–121. https://doi.org/10.1108/JQME-04-2013-0018

[299] Do P, Barros A, Bérenguer C, Bouvard K, Brissaud F. Dynamic grouping maintenance with time limited opportunities. Reliability Engineering & System Safety. 2013;120:51–59. https://doi.org/10.1016/j.ress.2013.03.016

[300] Vu HC, Do P, Barros A, Bérenguer C. Maintenance planning and dynamic grouping for multi-component systems with positive and negative economic dependencies. IMA Journal of Management Mathematics. 2015;26(2):145–170. https://doi.org/10.1093/imaman/dpu007

[301] Vu HC, Do P, Barros A, Bérenguer C. Maintenance grouping strategy for multi-component systems with dynamic contexts. Reliability Engineering & System Safety. 2014;132:233–249.

[302] Do P, Vu HC, Barros A, Bérenguer C. Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams. Reliability Engineering & System Safety. 2015;142:56–67. https://doi.org/10.1016/j.ress.2015.04.022

[303] Hong HP, Zhou W, Zhang S, Ye W. Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components. Reliability Engineering & System Safety. 2014;121:276–288. https://doi.org/10.1016/j.ress.2013.09.004

[304] Rasmekomen N, Parlikad AK. Condition-based maintenance of multi-component systems with degradation state-rate interactions. Reliability Engineering & System Safety. 2016;148:1–10. https://doi.org/10.1016/j.ress.2015.11.010

[305] Zhang N, Fouladirad M, Barros A, Zhang J. Condition-based maintenance for a k-out-of-n deteriorating system under periodic inspection with failure dependence. European Journal of Operational Research. 2020;287(1):159–167. https://doi.org/10.1016/j.ejor.2020.04.041

[306] Nguyen KA, Do P, Grall A. Multi-level predictive maintenance for multi-component systems. Reliability Engineering & System Safety. 2015;144:83–94. https://doi.org/10.1016/j.ress.2015.07.017

[307] Oakley JL, Wilson KJ, Philipson P. A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence. Reliability Engineering & System Safety. 2022;222:108321.

[308] Dinh DH, Do P, Iung B. Multi-level opportunistic predictive maintenance for multi-component systems with economic dependence and assembly/disassembly impacts. Reliability Engineering & System Safety. 2022;217:108055. https://doi.org/10.1016/j.ress.2021.108055

[309] Do P, Assaf R, Scarf P, Iung B. Modelling and application of condition-based maintenance for a two-component system with stochastic and economic dependencies. Reliability Engineering & System Safety. 2019;182:86–97. https://doi.org/10.1016/j.ress.2018.10.007

[310] Xu J, Liang Z, Li YF, Wang K. Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance. Reliability Engineering & System Safety. 2021;211:107592.

[311] Shi Y, Zhu W, Xiang Y, Feng Q. Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement. Reliability Engineering & System Safety. 2020;202.

[312] Zhou X, Huang K, Xi L, Lee J. Preventive maintenance modeling for multi-component systems with considering stochastic failures and disassembly sequence. Reliability Engineering & System Safety. 2015;142:231–237.

[313] Cavalcante CAV, Lopes RS, Scarf PA. A general inspection and opportunistic replacement policy for one-component systems of variable quality. European Journal of Operational Research. 2018;266(3):911–919. https://doi.org/10.1016/j.ejor.2017.10.032

[314] Truong Ba H, Cholette ME, Borghesani P, Zhou Y, Ma L. Opportunistic maintenance considering non-homogenous opportunity arrivals and stochastic opportunity durations. Reliability Engineering & System Safety. 2017;160:151–161. https://doi.org/10.1016/j.ress.2016.12.011

[315] Ait Mokhtar EH, Chateauneuf A, Laggoune R. Condition based opportunistic preventive maintenance policy for utility systems with both economic and structural dependencies - application to a gas supply network. International Journal of Pressure Vessels and Piping. 2018;165:214–223. https://doi.org/10.1016/j.ijpvp.2018.07.001

[316] Lu B, Zhou X. Opportunistic preventive maintenance scheduling for serial-parallel multistage manufacturing systems with multiple streams of deterioration. Reliability Engineering & System Safety. 2017;168:116–127.

[317] Liu G, Chen S, Ho T, Ran X, Mao B, Lan Z. Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy. Reliability Engineering & System Safety. 2022;225:108572.

[318] Shen J, Zhang Y, Ma Y, Lin C. A novel opportunistic maintenance strategy for systems with dependent main and auxiliary components. IMA Journal of Management Mathematics. 2021;32(1):69–90.

[319] Zhou X, Ning X. Maintenance gravity window based opportunistic maintenance scheduling for multi-unit serial systems with stochastic production waits. Reliability Engineering & System Safety. 2021;215.

[320] Liu G, Chen S, Jin H, Liu S. Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance. Reliability Engineering & System Safety. 2021;213.

[321] Okoh P. Maintenance grouping optimization for the management of risk in offshore riser system. Process Safety and Environmental Protection. 2015;98:33–39.

[322] Nguyen HSH, Do P, Vu HC, Iung B. Dynamic maintenance grouping and routing for geographically dispersed production systems. Reliability Engineering & System Safety. 2019;185:392–404.

[323] Uit Het Broek MAJ, Teunter RH, de Jonge B, Veldman J. Joint condition-based maintenance and load-sharing optimization for two-unit systems with economic dependency. European Journal of Operational Research. 2021;295(3):1119–1131.

[324] Zhang N, Fouladirad M, Barros A. Maintenance analysis of a two-component load-sharing system. Reliability Engineering & System Safety. 2017;167:67–74.

[325] Vu HC, Do P, Fouladirad M, Grall A. Dynamic opportunistic maintenance planning for multi-component redundant systems with various types of opportunities. Reliability Engineering & System Safety. 2020;198:106854. https://doi.org/10.1016/j.ress.2020.106854

[326] Liang Z, Parlikad AK. Predictive group maintenance for multi-system multi-component networks. Reliability Engineering & System Safety. 2020;195.

[327] Iung B, Do P, Levrat E, Voisin A. Opportunistic maintenance based on multi-dependent components of manufacturing system. CIRP Annals. 2016;65(1):401–404. https://doi.org/10.1016/j.cirp.2016.04.063

[328] Mena R, Viveros P, Zio E, Campos S. An optimization framework for opportunistic planning of preventive maintenance activities. Reliability Engineering & System Safety. 2021;215.

[329] Wu S, Chen Y, Wu Q, Wang Z. Linking component importance to optimisation of preventive maintenance policy. Reliability Engineering & System Safety. 2016;146:26–32.

[330] Zequeira RI, Bérenguer C. On the inspection policy of a two-component parallel system with failure interaction. Reliability Engineering & System Safety. 2005;88(1):99–107.

[331] Castanier B, Grall A, Bérenguer C. A condition-based maintenance policy with non-periodic inspections for a two-unit series system. Reliability Engineering & System Safety. 2005;87(1):109–120.

[332] Golmakani HR, Moakedi H. Periodic inspection optimization model for a two-component repairable system with failure interaction. Computers & Industrial Engineering. 2012;63(3):540–545. https://doi.org/10.1016/j.cie.2011.07.020

[333] Scarf PA, Deara M. Block replacement policies for a two-component system with failure dependence. Naval Research Logistics. 2003;50(1):70–87. https://doi.org/10.1002/nav.10051

[334] Marseguerra M, Zio E, Podofillini L. Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation. Reliability Engineering & System Safety. 2002;77(2):151–165. https://doi.org/10.1016/S0951-8320(02)00043-1

[335] Camci F. System maintenance scheduling with prognostics information using genetic algorithm. IEEE Transactions on Reliability. 2009;58(3):539–552. https://doi.org/10.1109/TR.2009.2026818

[336] Sheikhalishahi M, Azadeh A, Pintelon L. Dynamic maintenance planning approach by considering grouping strategy and human factors. Process Safety and Environmental Protection. 2017;107:289–298.

[337] Sheikhalishahi M, Pintelon L, Azadeh A. An integrated approach for maintenance planning by considering human factors: Application to a petrochemical plant. Process Safety and Environmental Protection. 2017;109:400–409.

[338] Van Horenbeek A, Buré J, Cattrysse D, Pintelon L, Vansteenwegen P. Joint maintenance and inventory optimization systems: A review. International Journal of Production Economics. 2013;143(2):499–508. https://doi.org/10.1016/j.ijpe.2012.04.001

[339] Zhang X, Zeng J. Joint optimization of condition-based opportunistic maintenance and spare parts provisioning policy in multiunit systems. European Journal of Operational Research. 2017;262(2):479–498.

[340] Okonkwo CS, Ogunwole O, Okeke OT. Framework for strategic procurement optimization in oil and gas operations. Iconic Research and Engineering Journals. 2018;1(7):153–168. https://doi.org/10.64388/IREV1I7-1713119

[341] Ogunwole O, Okonkwo CS, Agbabiaka J, Mayo W, Okeke OT. Supply chain resilience framework for critical infrastructure and gas processing plants. Shodhshauryam, International Scientific Refereed Research Journal. 2021;4(4):444–461. https://doi.org/10.32628/SHISRRJ214462

[342] Agbabiaka J, Okonkwo CS, Ogunwole O, Mayo W, Okeke OT. Supply chain risk management model for EPC and gas processing projects. Iconic Research and Engineering Journals. 2019;3(2):968–980. https://doi.org/10.64388/IREV3I2-1713124

[343] Caroni C. “Failure limited” data and TTT-based trend tests in multiple repairable systems. Reliability Engineering & System Safety. 2010;95(6):704–706. https://doi.org/10.1016/j.ress.2010.02.007

[344] Louit DM, Pascual R, Jardine AKS. A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data. Reliability Engineering & System Safety. 2009;94(10):1618–1628.

[345] Taghipour S, Banjevic D. Trend analysis of the power law process using expectation-maximization algorithm for data censored by inspection intervals. Reliability Engineering & System Safety. 2011;96(10):1340–1348. https://doi.org/10.1016/j.ress.2011.03.018

[346] Asfaw ZG, Lindqvist BH. Unobserved heterogeneity in the power law nonhomogeneous poisson process. Reliability Engineering & System Safety. 2015;134:59–65.

[347] Slimacek V, Lindqvist BH. Nonhomogeneous poisson process with nonparametric frailty. Reliability Engineering & System Safety. 2016;149:14–23.

[348] Slimacek V, Lindqvist BH. Nonhomogeneous poisson process with nonparametric frailty and covariates. Reliability Engineering & System Safety. 2017;167:75–83.

[349] Nafisah I, Shrahili M, Alotaibi N, Scarf P. Virtual series-system models of imperfect repair. Reliability Engineering & System Safety. 2019;188:604–613.

[350] Doyen L. Asymptotic properties of imperfect repair models and estimation of repair efficiency. Naval Research Logistics. 2010;57(3):296–307.

[351] Nguyen DT, Dijoux Y, Fouladirad M. Analytical properties of an imperfect repair model and application in preventive maintenance scheduling. European Journal of Operational Research. 2017;256(2):439–453.

[352] Liu X, Finkelstein M, Vatn J, Dijoux Y. Steady-state imperfect repair models. European Journal of Operational Research. 2020;286(2):538–546.

[353] Huynh KT. Modeling past-dependent partial repairs for condition-based maintenance of continuously deteriorating systems. European Journal of Operational Research. 2020;280(1):152–163.

[354] Compare M, Antonello F, Pinciroli L, Zio E. A general model for life-cycle cost analysis of condition-based maintenance enabled by PHM capabilities. Reliability Engineering & System Safety. 2022;224.

[355] Dui H, Li S, Xing L, Liu H. System performance-based joint importance analysis guided maintenance for repairable systems. Reliability Engineering & System Safety. 2019;186:162–175.

[356] Chen L, Cheng C, Dui H, Xing L. Maintenance cost-based importance analysis under different maintenance strategies. Reliability Engineering & System Safety. 2022;222.

[357] Joanni A, Rackwitz R. Cost-benefit optimization for maintained structures by a renewal model. Reliability Engineering & System Safety. 2008;93(3):489–499.

[358] Ching J, Leu SS. Bayesian updating of reliability of civil infrastructure facilities based on condition-state data and fault-tree model. Reliability Engineering & System Safety. 2009;94(12):1962–1974.

[359] Gerbec M. A reliability analysis of a natural-gas pressure-regulating installation. Reliability Engineering & System Safety. 2010;95(11):1154–1163. https://doi.org/10.1016/j.ress.2010.06.022

[360] Li R, Arzaghi E, Abbassi R, Chen D, Li C, Li H, et al. Dynamic maintenance planning of a hydro-turbine in operational life cycle. Reliability Engineering & System Safety. 2020;204.

[361] Diaz-Bejarano E, Behranvand E, Coletti F, Mozdianfard MR, Macchietto S. Organic and inorganic fouling in heat exchangers - industrial case study: Analysis of fouling state. Applied Energy. 2017;206:1250–1266. https://doi.org/10.1016/j.apenergy.2017.10.018

[362] Markowski M, Trafczynski M, Urbaniec K. Identification of the influence of fouling on the heat recovery in a network of shell and tube heat exchangers. Applied Energy. 2013;102:755–764.

[363] Pan M, Bulatov I, Smith R. Improving heat recovery in retrofitting heat exchanger networks with heat transfer intensification, pressure drop constraint and fouling mitigation. Applied Energy. 2016;161:611–626.

[364] Hang P, Zhao L, Liu G. Optimal design of heat exchanger network considering the fouling throughout the operating cycle. Energy. 2022;241. https://doi.org/10.1016/j.energy.2021.122913

[365] Li YG, Nilkitsaranont P. Gas turbine performance prognostic for condition-based maintenance. Applied Energy. 2009;86(10):2152–2161.

[366] Zhou D, Zhang H, Weng S. A novel prognostic model of performance degradation trend for power machinery maintenance. Energy. 2014;78:740–746.

[367] de Castro-Cros M, Velasco M, Angulo C. Machine-learning-based condition assessment of gas turbines - a review. Energies. 2021;14(24).

[368] Safiyullah F, Sulaiman SA, Naz MY, Jasmani MS, Ghazali SMA. Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming. Energy. 2018;158:485–494.

[369] Che H, Zeng S, Guo J, Wang Y. Reliability modeling for dependent competing failure processes with mutually dependent degradation process and shock process. Reliability Engineering & System Safety. 2018;180:168–178.

[370] Carr MJ, Christer AH. Incorporating the potential for human error in maintenance models. Journal of the Operational Research Society. 2003;54(12):1249–1253.

[371] Noroozi A, Khakzad N, Khan F, MacKinnon S, Abbassi R. The role of human error in risk analysis: Application to pre- and post-maintenance procedures of process facilities. Reliability Engineering & System Safety. 2013;119:251–258. https://doi.org/10.1016/j.ress.2013.06.038

[372] Asadzadeh SM, Azadeh A. An integrated systemic model for optimization of condition-based maintenance with human error. Reliability Engineering & System Safety. 2014;124:117–131.

[373] Heo G, Park J. A framework for evaluating the effects of maintenance-related human errors in nuclear power plants. Reliability Engineering & System Safety. 2010;95(7):797–805.

[374] Ramos MA, López Droguett E, Mosleh A, Das Chagas Moura M. A human reliability analysis methodology for oil refineries and petrochemical plants operation: Phoenix-PRO qualitative framework. Reliability Engineering & System Safety. 2020;193.

[375] Kariuki SG, Löwe K. Integrating human factors into process hazard analysis. Reliability Engineering & System Safety. 2007;92(12):1764–1773.

[376] Antonovsky A, Pollock C, Straker L. System reliability as perceived by maintenance personnel on petroleum production facilities. Reliability Engineering & System Safety. 2016;152:58–65.

[377] Obogo SF, Obriki OD, Arumosoye OM. Conceptual model for incident prevention in industrial maintenance engineering environments. International Journal of Multidisciplinary Research and Growth Evaluation. 2021;2(6):847–858. https://doi.org/10.54660/IJMRGE.2021.2.6.847-858

[378] Obriki OD, Arumosoye OM. Conceptual modeling of data-driven occupational safety risk control in large-scale energy infrastructure projects. Iconic Research and Engineering Journals. 2018;1(7):169–189. https://doi.org/10.64388/IREV1I7-1714414

[379] Nwangwu CV, Duvbiama-Owasanoye OA, Onwumere A. A framework for operational integrity in high-risk drilling: Balancing productivity and safety compliance. Iconic Research and Engineering Journals. 2018;2(5):436–453.

@article{1724116,
      author = {Paul Chibuike Obetta, Susan Ochi Makut, Festus-Michael Uwagboe, Henry Chukwuemeka Olisakwe},
      title = {Reliability-Centered Maintenance and Mechanical Integrity Management for Aging Onshore Oil and Gas Production Facilities},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {5},
      pages = {343-384},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724116.pdf},
      abstract = {Many onshore oil and gas production facilities now operate beyond their design life, yet their maintenance programmes still follow calendar intervals set when the equipment was new, ignoring rising corrosion and hidden-failure rates. This study develops an integrated reliability-centered maintenance and mechanical integrity framework for a representative 25-year-old onshore facility, evaluated over a 15-year life extension. Active failure modes are optimized with Weibull renewal and delay-time models under a risk cap for safety and environmental consequences. Static equipment is managed by risk-based inspection with corrosion-rate updating and inspection effectiveness. Protective devices receive age-dependent proof-test intervals. Inputs are representative published values, not field data. Against the legacy calendar programme, reliability-centered task selection cut rotating-equipment failures by 53 percent and cost by 26 percent. Static-equipment loss of containment events fell from 16.9 to 7.1 with static intervals and to 4.6 with age-adaptive inspection. Only the age-adaptive strategy kept every safety instrumented function within its SIL 2 limit, and it reduced failures on demand by 60 percent. The integrated strategy cost USD 23.5 million over 15 years, 23 percent less than legacy and 5 percent more than static reliability-centered maintenance. That premium averts about one additional loss of containment or failure on demand per USD 0.4 million. The advantage widens as corrosion accelerates. With five failure records, poorly estimated failure models raised expected cost by 22 percent.},
      keywords = {reliability-centered maintenance; mechanical integrity; ageing facilities; risk-based inspection; delay-time model; proof testing; onshore oil and gas},
      month = {November},
      doi = {https://doi.org/10.64388/IREV6I5-1724116}
  }