Optimal Bayesian early fault detection for CNC equipment using hidden semi-Markov process

• Published in: Mechanical systems and signal processing Vol. 122; pp. 290 - 306
• Main Authors: Duan, Chaoqun, Makis, Viliam, Deng, Chao
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.05.2019; Elsevier BV
• Subjects: Algorithms; Autoregressive models; Bayesian analysis; Condition-based maintenance; Control charts; Control equipment; Cycle time; Degradation; Early fault detection; Economic models; Fault detection; Hidden semi-Markov model; Life cycle assessment; Life prediction; Markov analysis; Markov chains; Mean residual life; Modelling; Multivariate analysis; Multivariate Bayesian control policy; Numerical controls; Optimal control; Optimization; Parameter estimation; Useful life; Hidden semi-Markov model; Mean residual life; Condition-based maintenance; Early fault detection; Multivariate Bayesian control policy
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2018.11.040

•Novel approach to the cost-optimal early fault detection of CNC equipment.•VAR modeling of multivariate positioning data and residual testing for control purposes.•Estimation of the remaining useful life of CNC equipment using Bayesian approach.•Presented method outperforms previously published method based on HMM. The goal of a computer numerically controlled (CNC) equipment is to guarantee high specified performance and to maintain it effectively over its life cycle time. Thus, the assessment of health condition is crucial for industrial systems. However, very few papers have dealt with the cost-optimal early fault detection and remaining useful life prediction of CNC equipment using multivariate positioning data. The novel approach presented here is based on vector autoregressive (VAR) degradation modeling and hidden semi-Markov modeling using the optimal Bayesian control technique. System condition is modeled using a continuous time semi-Markov chain with three states, i.e. unobservable healthy state 1, unobservable warning state 2 and observable failure state 3. Model parameter estimates are calculated using the expectation-maximization (EM) algorithm. The optimal control policy for the three-state model is represented by a Bayesian control chart for a multivariate observation process. The optimization problem is formulated and solved in the semi-Markov decision process (SMDP) framework. A formula for the mean residual life (MRL) is also derived based on Bayesian approach, which enables the estimation of the remaining useful life and early maintenance planning based on the observed data. The validation of the proposed methodologies is carried out using actual multivariate degradation data obtained from a CNC equipment. A comparison with the multivariate Bayesian control chart based on a hidden Markov model (HMM) is given, which illustrates the effectiveness of the proposed approach.