Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
561272 | Mechanical Systems and Signal Processing | 2013 | 22 Pages |
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.
► A generative topographic mapping-based method is proposed for machine health monitoring. ► Negative likelihood probability is capable of quantifying machine health states. ► A probabilistic indication is developed to analyze machine failure. ► A contribution analysis method can discover effective features. ► The results on machine testbed illustrate the effectiveness of the proposed method.