Abstract
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.
Highlights
Serious losses happen often in practice due to accidental system failure and the lack of online message of remaining useful life (RUL) and the performance reliability
5 Conclusions RUL assessment and modeling have become increasingly important in system reliability and Prognostic and Health Management (PHM)
System health management involves determining the system performance status and the RUL of critical systems used for the maintenance plan, decision making, and system global optimization
Summary
Serious losses happen often in practice due to accidental system failure and the lack of online message of remaining useful life (RUL) and the performance reliability. To assess the non-observable degradation of the device from measurable observations of the performance, hidden Markov modeling is adopted. The state space model consist of state and observation equations, presenting a first order HMM, is convenient for modeling multivariate data and nonlinear/ non-Gaussian processes, with significant advantages over traditional time-series techniques [5, 11, 12]. For online predictions based on the state-space model (SSM), the recursive assessment of the posteriori distributions of xt, which modeling the degradation, is of a great concern.
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