Abstract
Comprehensive risk assessment plays a significant role in railway rolling stock safety planning to prevent accidents, including rail derailment and collision. Several methods of evaluating individual sources of railway system risk, ranging from human factors to inherent system failure and environmental hazards, exist in the literature. However, the lack of a hybrid technique to integrate these multiple sources of risk holistically, including their interdependent effects, as a single framework for robust, accurate, and comprehensive risk assessment can limit risk perception and risk mitigation actions. This report proposes a dynamic hybrid model (DHM) that incorporates the Bayesian convolutional factorization and elimination method as a compound aggregation of frequency and severity distributions. The DHM validates predicted risk using Bayesian expectation–maximization machine learning with evidenced-based propagation from expert knowledge and learned data. It also incorporates sensitivity analysis to improve the predicted risk further by prioritizing the hazards with the maximum impact on the estimated risk due to organization resource constraints. A railway case study in the UK revealed that risk prediction using the DHM provided a holistic view of the risk. The results showed that the quantitative risk prediction using the DHM was significantly more robust, accurate, and holistic than that of the conventional risk-assessment method based on the inherent failure rate. This research will facilitate the comprehensive development of risk-mitigation strategies, such as improvements in staff training and wiring insulation, to decrease the likelihood of train derailment caused by semi-permanent coupler failure.
Highlights
Despite the technological advancements and developments in autonomous machines, uncertainties caused by internal and external factors still influence the risk profiles of critical industrial physical assets, such as rolling stock (RS)
The proposed method incorporates n − fold and N − fold convolutions in a causal Bayesian dependency model. It uses the advanced features of Bayesian factorization and elimination (BFE) theory to ensure that expert knowledge and information learned from the training data can be validated automatically using the expectation–maximization (EM) technique
The objective of this case study was to assess the overall risk for a three-car electric multiple unit (EMU) fleet based in the UK similar to Fig. 1 considering coupler failure rate, human error conditions during maintenance, and electromagnetic interference (EMI) from the rail infrastructure
Summary
Despite the technological advancements and developments in autonomous machines, uncertainties caused by internal and external factors still influence the risk profiles of critical industrial physical assets, such as rolling stock (RS). The concept of information or technique hybridization is well-established as a means of combining various techniques to ensure that the weaknesses in some can be compensated by the strengths of others, creating a synergy that enhances the overall robustness of the outcomes Following this idea, this report proposes a novel dynamic hybrid model (DHM) and demonstrates its proficiency as a comprehensive risk estimation approach for complex systems under multiple dynamic conditions. The proposed method incorporates n − fold and N − fold convolutions in a causal Bayesian dependency model It uses the advanced features of Bayesian factorization and elimination (BFE) theory to ensure that expert knowledge and information learned from the training data can be validated automatically using the expectation–maximization (EM) technique.
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