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Reliability Engineering and Failure Analysis in Complex Engineering Systems
Subject area: Science,Engineering and Technology · Area of research: Reliability and Failure Analysis
Abstract
This review examines the conceptual foundations, analytical methods, lifecycle applications, and emerging technologies that define contemporary reliability engineering and failure analysis in complex systems. Its purpose is to clarify how dependable performance can be designed, assessed, sustained, and improved where technical components, software, human actions, organisational structures, and environmental conditions interact. A structured narrative review was adopted, drawing together established theories, quantitative models, failure-investigation techniques, maintenance strategies, resilience principles, and digital approaches from diverse engineering contexts. The findings show that reliability is fundamentally systemic rather than component-centred. Statistical life-data analysis, fault trees, Bayesian networks, Markov models, redundancy optimisation, and reliability allocation provide essential quantitative support, but their value depends on realistic assumptions regarding dependence, uncertainty, and operating conditions. Structured methods such as FMEA, FMECA, HAZOP, root-cause analysis, Bow-Tie Analysis, and cause-and-effect analysis were found to offer complementary strengths in hazard identification, causal diagnosis, criticality assessment, and corrective-action prioritisation. Physical examination, non-destructive testing, signal analysis, and data-driven diagnostics further improve investigative accuracy when integrated with operational evidence. The review also demonstrates that design for reliability, accelerated testing, condition monitoring, prognostics, and maintenance optimisation strengthen availability, safety, productivity, and lifecycle value. Artificial intelligence and digital twins create significant predictive opportunities, yet their implementation remains constrained by data quality, cybersecurity, interoperability, explainability, workforce capability, and organisational readiness. It is concluded that reliability should be governed as an integrated lifecycle function. Organisations should strengthen multidisciplinary analysis, risk-based maintenance, validated digital tools, and learning systems. Future research should prioritise interpretable intelligence, cascading-failure modelling, uncertainty-aware prognostics, cyber-physical resilience, and field-based studies in resource-constrained economies across critical infrastructure sectors.
Keywords
reliability engineering; failure analysis; system dependability; condition monitoring; digital twins; maintenance optimisation.
How to cite this paper
@article{1722490,
author = {Edikan Nse Gideon, Tunji S. Adaramola, Samuel Fadero},
title = {Reliability Engineering and Failure Analysis in Complex Engineering Systems},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {2},
number = {11},
pages = {703-727},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722490.pdf},
abstract = {This review examines the conceptual foundations, analytical methods, lifecycle applications, and emerging technologies that define contemporary reliability engineering and failure analysis in complex systems. Its purpose is to clarify how dependable performance can be designed, assessed, sustained, and improved where technical components, software, human actions, organisational structures, and environmental conditions interact. A structured narrative review was adopted, drawing together established theories, quantitative models, failure-investigation techniques, maintenance strategies, resilience principles, and digital approaches from diverse engineering contexts. The findings show that reliability is fundamentally systemic rather than component-centred. Statistical life-data analysis, fault trees, Bayesian networks, Markov models, redundancy optimisation, and reliability allocation provide essential quantitative support, but their value depends on realistic assumptions regarding dependence, uncertainty, and operating conditions. Structured methods such as FMEA, FMECA, HAZOP, root-cause analysis, Bow-Tie Analysis, and cause-and-effect analysis were found to offer complementary strengths in hazard identification, causal diagnosis, criticality assessment, and corrective-action prioritisation. Physical examination, non-destructive testing, signal analysis, and data-driven diagnostics further improve investigative accuracy when integrated with operational evidence. The review also demonstrates that design for reliability, accelerated testing, condition monitoring, prognostics, and maintenance optimisation strengthen availability, safety, productivity, and lifecycle value. Artificial intelligence and digital twins create significant predictive opportunities, yet their implementation remains constrained by data quality, cybersecurity, interoperability, explainability, workforce capability, and organisational readiness. It is concluded that reliability should be governed as an integrated lifecycle function. Organisations should strengthen multidisciplinary analysis, risk-based maintenance, validated digital tools, and learning systems. Future research should prioritise interpretable intelligence, cascading-failure modelling, uncertainty-aware prognostics, cyber-physical resilience, and field-based studies in resource-constrained economies across critical infrastructure sectors.},
keywords = {reliability engineering; failure analysis; system dependability; condition monitoring; digital twins; maintenance optimisation.},
month = {May},
}