An Intelligent Fault Diagnosis Method of Rolling Bearings via Variational Mode Decomposition and Common Spatial Pattern-Based Feature Extraction

摘要

Monitoring and identifying the health condition of rolling bearings can reduce the risk of mechanical equipment failure. This paper proposes a novel intelligent diagnosis method of rolling bearings: First, the vibration signals are decomposed into band-limited instinct mode functions (BLIMFs) by variational mode decomposition (VMD). Then, the proposed high-dimensional common spatial pattern (hdCSP) filter is used to generate the high-dimensional eigenvectors representing the decomposed BLIMFs. Finally, the random forests classifier is used to classify the eigenvectors and obtain the diagnosis results. The performance of the proposed VMD-hdCSP method is evaluated on the Case Western Reserve University dataset. The experimental results show the proposed method can automatically classify different health states of rolling bearings and obtain precise diagnosis results.

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