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The health monitoring and management have been accepted in modern industrial machinery for an intelligent industrial production. To timely and reliably assess the bearing performance degradation, a novel health monitoring method called feature clustering analysis (FCA) has been proposed in this study. Along with the working time going, this new monitored chart picked by FCA aims to describe the feature clustering distribution transition by a series of reference models. First, the data provided by the reference state (healthy data) and the one from the monitor state (monitor data) are fused together to construct a reference model, which is to explore the active role of healthy status and activate the difference between healthy status and unhealthy status. Manifold learning is later implemented to mine the discriminated features for good class-separable clustering measure. In this manner, heterogeneous information hidden in this reference model will appear once degradation happened. Finally, a clustering quantification factor, named as feature clustering indicator (FCI), is calculated to assess distribution evolution and migration of the monitor status as compared to the consistent healthy status. Furthermore, a single Gaussian model (SGM) based on these FCIs is used to provide a smooth estimate of the healthy condition level. The corresponding negative log likelihood probability (NLLP) and the fault occurrence alarm are developed for an accurate and reliable FCC. And it can well depict a comprehensibility of the real bearing performance degradation process for its whole life. Meanwhile, as compared to other health profiles based on the classical health indicators, the proposed FCC has provided a much more accurate degradation level and rather monotonic profile. The experimental results show the potential in machine health performance degradation assessment.

Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment