Joint Online RUL Prediction for Multivariate Deteriorating Systems

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 5; pp. 2870 - 2878
• Main Authors: Peng, Weiwen, Ye, Zhi-Sheng, Chen, Nan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-05-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Degradation; Degradation modeling; Filtration; Informatics; Mathematical models; Measurement errors; Numerical models; online inference; Parameter estimation; Parameter identification; particle filter; Predictive models; Prognostics and health management; prognostics and health management (PHM); remaining useful life (RUL); Stochastic processes
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2018.2869429

Stochastic processes and filtering methods are popular tools for degradation modeling and online remaining useful life (RUL) prediction. However, most models are for one-dimensional degradation and various filtering methods can only handle observations from a single system. This paper studies joint online RUL prediction of multideteriorating systems with multisystem observations and measurement errors. A multivariate degradation model equipped with a batch particle filter is developed and built for characterizing multiple dependent performance deteriorations with measurement errors in each system. The batch particle filter is developed for simultaneous online parameter estimation and degradation state identification by leveraging multisystem observations. A numerical example and a case study are provided to demonstrate the proposed method. The results show that homogeneous multisystem observations from a population of multideteriorating systems can be jointly processed on-the-fly. Individualized online RUL prediction with improved precision for each system can be achieved through the joint online inference.