Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Ya Wei Hu, Hong Chao Zhang, Shu Jie Liu, Hui Tian Lu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Online assessment of remaining useful life (RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However, there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device. In this paper, state space model (SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo (MCMC) to Sequential Monte Carlo (SMC) algorithm is presented in order to derive the optimal Bayesian estimation. In the context of nonlinear & non-Gaussian dynamic systems, SMC (also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.

Original languageEnglish
Article number5
JournalChinese Journal of Mechanical Engineering (English Edition)
Issue number1
StatePublished - Dec 1 2018


  • Bayesian estimation
  • Milling cutter lifetime
  • Particle filter
  • Remaining useful life
  • Sequential Monte Carlo method
  • State-space model
  • Stochastic processes


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