Fault diagnosis of rolling bearing based on multiscale one-dimensional hybrid binary pattern

Abstract As one of the most critical components in rotating machinery, it is essential to determine the health of rolling bearings on time. The effective feature extraction method is considered significant for fault diagnosis. In order to extract sufficient and effective bearing information, a novel extraction method called multiscale one-dimensional hybrid pattern (1D-HBP) is proposed for fault diagnosis of rolling bearings. The proposed method extracts the local and global texture statistical information of signals to reflect different bearing conditions. Considering the inherent multi-scale characteristics of the vibration signals, multiscale analysis is employed to obtain discriminative features of different scales. Two rolling bearings sets with changeable loads and rotating speeds verified the effectiveness and practicability of the proposed diagnostic model. Compared to other models examined for the same dataset our proposed model achieves a remarkably high classification accuracy.

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