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We aim to address the issues of difficult acquisition of bearing fault data, few feature data sets, and low efficiency of intelligent diagnosis. In this paper, an orthogonal wavelet transform K-nearest neighbor (OWTKNN) diagnosis method has been proposed. The (OWT) method extracts the peaks of each detail signal as training samples and uses the K-Nearest Neighbor (KNN) method for fault classification. The classification results of the multiple fault test data obtained through rolling bearing tests show that the method can reach a fault recognition rate of 100%, and compared with KNN without extracted eigenvalues, it significantly improves the classification effects from various unknown fault data of the bearing inner ring and ball, shortens classification time, and improves the intelligent diagnosis efficiency. In addition, it achieves an overall recognition rate exceeding 95%, Comparing OWT, EMD, and VMD feature extraction methods, both the OWTKNN and k-center point clustering algorithm do not exceed 80% (KCA), also bearing testimony of the effectiveness of this method.

An Orthogonal Wavelet Transform-Based K-Nearest Neighbor Algorithm to Detect Faults in Bearings