A nonlinear probabilistic method and contribution analysis for machine condition monitoring

作者：Yu Jianbo^*

来源：Mechanical Systems and Signal Processing, 2013, 37(1-2): 293-314.

DOI：10.1016/j.ymssp.2013.01.010

• Condition-based maintenance
• Machine health monitoring
• Generative topographic mapping
• Contribution analysis
• Feature selection

Health degradation assessment from normal to failure condition of machine part or system is a key element in condition-based maintenance (CBM) system. This paper proposes a generative topographic mapping (GTM) and contribution analysis-based method to perform machine health degradation assessment and monitoring. GTM-based negative likelihood probability (NLLP) is developed to offer a comprehensible indication for quantifying machine health states. A Bayesian-inference-based probability (BIP) calculation method is further developed to analyze the failure probability of the monitored machine or component. A variable replacing-based contribution analysis method is developed to discover potential features that are effective for the detection and assessment of machine health degradation in its whole life. The experimental results on a turbine engine simulation system and a bearing testbed illustrate plausibility and effectiveness of the proposed methods.

• 出版日期2013-6
• 单位上海大学