Print Email Facebook Twitter Predictive maintenance of systems subject to hard failure based on proportional hazards model Title Predictive maintenance of systems subject to hard failure based on proportional hazards model Author Hu, Jiawen (National University of Singapore) Chen, P. (TU Delft Statistics) Date 2020 Abstract The remaining useful lifetime (RUL) estimated from the in-situ degradation data has shown to be useful for online predictive maintenance. In the literature, the RUL is often estimated by assuming a soft-failure threshold for the degradation data. In practice, however, systems may not be subject to the degradation-induced soft failures. Instead, the systems are deemed to be fail when they cannot perform the intended function, and such failures are known as hard failures. Because there are no fixed thresholds for hard failures, the corresponding RUL estimation is not an easy task, which causes difficulties in finding the optimal maintenance schedule. In this study, a Weibull proportional hazards model is proposed to jointly model the degradation data and the failure time data. The degradation data are treated as the time-varying covariates so that the degradation does not directly lead to system failures, but increases the hazard rate of hard failures. A random-effects Wiener process is proposed to model the degradation data by considering the system heterogeneities. Based on the developed proportional hazards model, closed-form distribution of the RUL is derived upon each inspection and the optimal maintenance schedule is then obtained by minimizing the system maintenance cost. The proposed maintenance strategy is successfully applied to predictive maintenance of lead-acid batteries. Subject Condition-based maintenanceDegradation dataWeibull distributionWiener process To reference this document use: http://resolver.tudelft.nl/uuid:70ddc879-3333-4f06-a59c-ed25a5c68af3 DOI https://doi.org/10.1016/j.ress.2019.106707 Embargo date 2021-07-28 ISSN 0951-8320 Source Reliability Engineering & System Safety, 196 Part of collection Institutional Repository Document type journal article Rights © 2020 Jiawen Hu, P. Chen Files PDF 1_s2.0_S0951832019306404_main.pdf 1.75 MB Close viewer /islandora/object/uuid:70ddc879-3333-4f06-a59c-ed25a5c68af3/datastream/OBJ/view