Joint Modeling of Degradation and Lifetime Data for RUL Prediction of Deteriorating Products

Jiawen Hu, Qiuzhuang Sun, Zhi Sheng Ye, Qiang Zhou

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


Degradation is one of the major root causes of system failure. In some applications, the degradation levels are different upon failure, in which the fixed failure threshold assumption commonly adopted in the degradation literature may not hold. This article tackles the difficulty by jointly analyzing the system degradation and the lifetime data, which enables the corresponding remaining useful life (RUL) prediction. We treat the degradation level as a multiplicative time-varying covariate of the system hazard rate, where a random-effects Wiener process is adopted to model the degradation process. The model parameters are estimated under a Bayesian framework, and we also develop a particle filter method to update the estimates when new data are available. This makes the proposed model be able to realize online RUL prediction based on the in-situ system health state signals. Through case studies on lead-acid batteries and digital communication systems, the proposed model is shown to outperform existing methods in terms of the RUL prediction accuracy.

Original languageEnglish (US)
Article number9186315
Pages (from-to)4521-4531
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number7
StatePublished - Jul 2021


  • Proportional hazards model
  • particle filter
  • random-effects Wiener process
  • remaining useful life (RUL)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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