Health Condition Monitoring of Machines Based on Hidden Markov Model and Contribution Analysis

Degradation parameter from normal to failure condition of machine part or system is needed as an object of health monitoring in condition-based maintenance (CBM). This paper proposes a hidden Markov model (HMM) and contribution-analysis-based method to assess the machine health degradation. A dynamic principal component analysis (DPCA) is used to extract effective features from vibration signals, where inherent signal autocorrelation is considered. A novel machine health assessment indication, HMM-based Mahalanobis distance is proposed to provide a comprehensible indication for quantifying machine health states. A variable-replacing-based contribution analysis method is developed to discover the effective features that are responsible for the detection and assessment of machine health degradation in its whole life. The experimental results based on a bearing test bed show the plausibility and effectiveness of the proposed methods, which can be considered as the machine health degradation monitoring model.