Bearing Fault Diagnosis Method of Deep Convolutional Neural Network Based on Multiwavelet Decomposition

To tackle the challenge of convolutional neural network and its integration methods with dcnoising preprocessing methods struggling to effectively extract useful signal features amidst high noise environments and low-quality data, a deep convolutional neural network model based on the Geronimo-Hardin-Massopust multiwavelet decomposition (GHMMD-DCNN) is proposed. The model's concept revolves around deeply integrating the multiwavelet packet decomposition with the convolutional neural network. In other words, this involves the creation of multiple first-level multiwavelet decomposition layers to extract the low-frequency and high-frequency signal components, and these layers arc lined alternately with the convolutional layer. This approach enables the model to extract and learn the useful time-frequency information of the signal on a multiscale basis. The signal decomposition and the feature learning arc executed alternately, and robust feature extraction is realized even under strong noise conditions. Tests arc carried out using aerospace high-speed bearing vibration data under different working conditions. The results show that the proposed model is able to reach stable convergence quickly and the recognition accuracy surpasses 99. 9%. The proposed method showcases superior fault recognition accuracy and stability in the presence of significant noise interference compared to contrast methods, which demonstrates its excellent anti-noise ability. In the test of fewer training samples, the proposed method achieves an impressive average diagnosis accuracy of 91. 19% with only 60 training samples per class. This represents a 13. 19% enhancement over alternative methods, verifying the GHMMD-DCNN's exceptional low-sample generalization ability. © 2024 Xi'an Jiaotong University. All rights reserved.