Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

From Springer Nature via Jisc Publications RouterHistory: received 2020-07-24, rev-recd 2021-02-12, accepted 2021-03-31, registration 2021-03-31, pub-electronic 2021-05-11, online 2021-05-11, pub-print 2021-06Publication status: PublishedAbstract: Railway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements

Risk-informed support vector machine regression model for component replacement— A case study of railway flange lubricator

The railway-rolling stock wheel flange lubricator protects the wheels and railhead by lubricating their contacts. Failed or missing flange lubricators can lead to excessive wheel wear, wheel flats, wheel cracks, rolling contact fatigue, rail damage, and derailment accidents. In extreme cases, missing or worn flange lubricators due to nonlinear rail conditions may lead to fire hazards, particularly in underground rail infrastructure. In addition, the location of lubricators present accessibility issues and prolong the diagnosis of failure. This study therefore proposes an adaptive risk-based support vector regression (SVR) machine with a Gaussian kernel function that can accurately and proactively predict the wear loss of flange lubricators from a small data set. While most flange lubricators fail owing to wear loss, others fail owing to premature failure modes such as cracks and fatigue. The risk-informed feature evaluates failure rates associated with failures other than wear loss to support a balanced determination of the optimised replacement frequency. The proposed model was applied and validated as a case study for the London underground train. The findings showed that the optimised maintenance inspection of the flange lubricator, as a balance between safety and organisational resource constraints, was an average of every 4000 km between train operations