Hybrid adaptive model to optimise components replacement strategy: A case study of railway brake blocks failure analysis

In this paper, we propose a novel hybrid adaptive model (HAM) that integrates Gaussian mixture probabilistic machine learning (ML), Weibull time-to-failure feature, and value of information (VOI) techniques for complex engineering failure analysis. The objective is to establish an optimum components replacement intervention strategy for composite brake blocks of railway rolling stocks to better curtail failures and possible accidents. The HAM considers brake blocks as rudimentary systems for emergency brake application to prevent catastrophic rail accidents under nonlinear and dynamic environmental conditions. A Gaussian mixture regression technique with a rational quadratic kernel function is used to develop a predictive wear model that applies wear measurement as training data. The Weibull feature estimates the threshold maintenance replacement strategy pertaining to other premature failure modes before the brake blocks’ legal scrapping wear limit is reached. Finally, the VOI feature establishes the net loss in terms of cost for the proposed replacement options to guide optimum component replacement selection strategy under organisational resource constraints. Based on operational data obtained from several brake blocks of the London Underground Trains fleet, the proposed HAM failure analysis technique provided a better balance between safety and cost-effectiveness compared to other popular ML approaches.