A failure-dependency modeling and state discretization approach for condition-based maintenance optimization of multi-component systems. (April 2018) - Xu, Mengkai; Jin, Xiaoning; Kamarthi, Sagar; Noor-E-Alam, Md.
Highlights: We have proposed an extended proportional hazard model for a multi-component system with stochastic dependence. We developed a novel state discretization algorithm for evaluating the effect of component state on the hazard rate of another component. We developed a cost model in association with minimal repair and maintenance to optimize the system-level threshold. A real-world case study has been carried out to show the effectiveness of our proposed model. Abstract: Unexpected component failures in a mechanical system always cause loss of performance and functionality of the entire system. Condition-based maintenance decisions for a multi-component mechanical system are challenging because the interdependence of individual components' degradation is not fully understood and lack of physical models. Most existing literature commonly assumes that degradation and failure of individual components within a mechanical system are independent, which could lead to inaccurate diagnostic and prognostic results. In this research, state-rate dependence denoting interaction between component health condition (degradation state) and failure rate is proposed for degradation and failure analysis for a two-component repairable system. A state discretization technique is proposed to model how health state of one component affects the hazard rate of another. An extended proportional hazard model (PHM) is used to characterize the failure dependence and estimate the influence of degradation state of one component on the hazard rate of another. An optimization model is developed to determine the optimal hazard-based threshold for a two-component repairable system. A case study on a generic industrial gearbox has been conducted to show the effectiveness of the proposed model.