AI-Driven Predictive Maintenance of Industrial Fire Protection Machinery: A Reliability-Based Engineering Framework for Saudi Manufacturing Facilities

References

[1] Zonta T, da Costa CA, Righi RR, de Lima MJ, Trindade ES, Li GP. Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering. 2020;150:106889. https://doi.org/10.1016/j.cie.2020.106889

[2] Leukel J, González J, Riekert M. Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review. Journal of Manufacturing Systems. 2021;61:87–96. https://doi.org/10.1016/j.jmsy.2021.08.012

[3] Fernandes M, Corchado JM, Marreiros G. Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review. Applied Intelligence. 2022;52:14246–14280. https://doi.org/10.1007/s10489-022-03344-3

[4] Nacchia M, Fruggiero F, Lambiase A, Bruton K. A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector. Applied Sciences. 2021;11:2546. https://doi.org/10.3390/app11062546

[5] Phan TLJ, Gehrhardt I, Heik D, Bahrpeyma F, Reichelt D. A systematic mapping study on machine learning techniques applied for condition monitoring and predictive maintenance in the manufacturing sector. Logistics. 2022;6:35. https://doi.org/10.3390/logistics6020035

[6] Polverino L, Abbate R, Manco P, Perfetto D, Caputo F, et al. Machine learning for prognostics and health management of industrial mechanical systems and equipment: A systematic literature review. International Journal of Engineering Business Management. 2023; 15, article 18479790231186848. https://doi.org/10.1177/18479790231186848

[7] Chen C, Fu H, Zheng Y, Tao F, Liu Y. The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems. 2023;71:581–594. https://doi.org/10.1016/j.jmsy.2023.10.010

[8] van Dinter R, Tekinerdogan B, Catal C. Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology. 2022;151:107008. https://doi.org/10.1016/j.infsof.2022.107008

[9] Zhong D, Xia Z, Zhu Y, Duan J. Overview of predictive maintenance based on digital twin technology. Heliyon. 2023;9:e14534. https://doi.org/10.1016/j.heliyon.2023.e14534

[10] Deng W, Nguyen KTP, Medjaher K, Gogu C, Morio J. Physics-informed machine learning in prognostics and health management: State of the art and challenges. Applied Mathematical Modelling. 2023;124:325–352. https://doi.org/10.1016/j.apm.2023.07.011

[11] Li H, Zhang Z, Li T, Si X. A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities. Mechanical Systems and Signal Processing. 2024;209:111120. https://doi.org/10.1016/j.ymssp.2024.111120

[12] de Beaulieu MH, Jha MS, Garnier H, Cerbah F. Remaining Useful Life prediction based on physics-informed data augmentation. Reliability Engineering & System Safety. 2024;252:110451. https://doi.org/10.1016/j.ress.2024.110451

[13] Wu Y, Sicard B, Gadsden SA. Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring. Expert Systems with Applications. 2024;255:124678. https://doi.org/10.1016/j.eswa.2024.124678

[14] da Silva RF, Melani AHA, Michalski MAC, de Souza GFM. Reliability and risk centered maintenance: A novel method for supporting maintenance management. Applied Sciences. 2023;13:10605. https://doi.org/10.3390/app131910605

[15] Bahrami M, Givehchi S. A novel approach for determining the reliability of sprinkler systems: A case study. Results in Engineering. 2023;17:100843. https://doi.org/10.1016/j.rineng.2022.100843

[16] Zhang S, Li Y, Chen X, Zhou R, Wu Z, Zarhmouti T. A novel IoT-based performance testing method and system for fire pumps. Water. 2024;16:792. https://doi.org/10.3390/w16050792

[17] Nguyen TP. AIoT fault detection for firefighting pump maintenance services based metaheuristics and combined deep learning methodologies. Computational Intelligence. 2025;41:e70071. https://doi.org/10.1111/coin.70071

[18] Almazrouei SM, Dweiri F, Aydin R, Alnaqbi A. A review on the advancements and challenges of artificial intelligence based models for predictive maintenance of water injection pumps in the oil and gas industry. Discover Applied Sciences. 2023;5:391. https://doi.org/10.1007/s42452-023-05618-y

[19] Nguyen MT, Huang JH. Fault detection in water pumps based on sound analysis using a deep learning technique. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2022;236. https://doi.org/10.1177/09544089211039304

[20] Aydin M, Sezer SI, Arici SS, Akyuz E. Predicting human reliability for emergency fire pump operational process on tanker ships utilising fuzzy Bayesian Network CREAM modelling. Ocean Engineering. 2024;314:119717. https://doi.org/10.1016/j.oceaneng.2024.119717

[21] Tamascelli N, Campari A, Parhizkar T, Paltrinieri N. Artificial intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning. Journal of Loss Prevention in the Process Industries. 2024;90:105343. https://doi.org/10.1016/j.jlp.2024.105343

[22] Khan F, Xu Z, Sun J, Khan FM, Ahmed A, Zhao Y. Recent advances in sensors for fire detection. Sensors. 2022;22:3310. https://doi.org/10.3390/s22093310

[23] Diaconu BM. Recent advances and emerging directions in fire detection systems based on machine learning algorithms. Fire. 2023;6:441. https://doi.org/10.3390/fire6110441

[24] Gawde S, Patil S, Kumar S, Kamat P, Kotecha K. An explainable predictive maintenance strategy for multi-fault diagnosis of rotating machines using multi-sensor data fusion. Decision Analytics Journal. 2024;10:100425. https://doi.org/10.1016/j.dajour.2024.100425

[25] Dereci U, Tuzkaya G. An explainable artificial intelligence model for predictive maintenance and spare parts optimization. Supply Chain Analytics. 2024;8:100078. https://doi.org/10.1016/j.sca.2024.100078

[26] Bala A, Rashid RZJA, Ismail I, et al. Artificial intelligence and edge computing for machine maintenance—review. Artificial Intelligence Review. 2024;57:119. https://doi.org/10.1007/s10462-024-10748-9

[27] Jiang GJ, Yang JS, Cheng TC, Sun HH. Remaining useful life prediction of rolling bearings based on Bayesian neural network and uncertainty quantification. Quality and Reliability Engineering International. 2023;39:1756–1774. https://doi.org/10.1002/qre.3308

[28] Fuller A, Fan Z, Day C, Barlow C. Digital twin: Enabling technologies, challenges and open research. IEEE Access. 2020;8:108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358

[29] Rasheed A, San O, Kvamsdal T. Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access. 2020;8:21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143

[30] Rajaoarisoa L, Randrianandraina R, Nalepa GJ, Gama J. Decision-making systems improvement based on explainable artificial intelligence approaches for predictive maintenance. Engineering Applications of Artificial Intelligence. 2025;139:109601. https://doi.org/10.1016/j.engappai.2024.109601

@article{1724645,
      author = {Talha Umar Khan, Waheed Mahboob Jilani},
      title = {AI-Driven Predictive Maintenance of Industrial Fire Protection Machinery: A Reliability-Based Engineering Framework for Saudi Manufacturing Facilities},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {149-163},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724645.pdf},
      abstract = {Industrial fire protection machinery has a very particular reliability requirement in that it has to be capable of performing when required, even though it will be in standby for the vast majority of its life. The conventional approach of scheduled inspection is still absolutely vital, though periodic testing may well miss the gradual degradation that occurs between inspections. This paper is a critical review of the potential for AI to augment rather than supplant the existing inspection and reliability engineering practices for fire pumps, drivers, valves, controllers and the associated suppression system in the Saudi manufacturing environment. It is a structured, interpretive synthesis of the peer-reviewed literature published between 2020 and 2025 on predictive maintenance, fire protection equipment, fault diagnosis, physics-informed prognostics and risk based decision making. The proposed contribution is a demand aware engineering framework which links operational signals, proof test results, component degradation, uncertainty and consequence severity to auditable maintenance decisions. Particular emphasis is placed on low frequency failures, environmental exposure, the unequal consequences of various types of failure and the difference between diagnostic accuracy and demonstrated system availability. The review identifies tangible opportunities in the area of multi-modal monitoring, hybrid reliability models and explainable maintenance prioritisation, whilst also highlighting the continuing problems with data portability, causal attribution, model calibration and field validation. A staged evaluation programme is recommended prior to any safety critical deployment. The key point is that prediction only becomes of operational value when it improves the evidence of readiness on demand and does not in any way compromise the requirement for mandatory proof testing or human responsibility.},
      keywords = {predictive maintenance; fire protection systems; fire pumps; reliability engineering; artificial intelligence; Saudi Arabia},
      month = {November},
  }