Abstract
In order to effectively identify the various faults of rolling bearings and the severity of the faults, an intelligent fault diagnosis system based on variational mode decomposition and multi-tree Mahalanobis Taguchi system with multiple mahalanobis distance was proposed. Vibration signals were decomposed into multiple BLIMFs by variational mode decomposition, and time domain and frequency domain features were extracted. The multiple mahalanobis distance method was used to solve the problem of many features in the diagnosis system. By using the advantages of Mahalanobis Taguchi system in features optimization, sensitive modal components for diagnosis and recognition were selected. Multi-tree Mahalanobis Taguchi system was constructed for intelligent identification of multiple fault states. Finally, rolling bearings fault data was tested to verify the accuracy of the algorithm and compared with other algorithms. The results show that the algorithm can simplify the diagnosis system, reduce training time and improve the recognition accuracy.
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