Intelligent fault diagnosis of gearbox based on differential continuous wavelet transform-parallel multi-block fusion residual network

• Published in: Measurement : journal of the International Measurement Confederation 2023-01, Vol.206, p.112318, Article 112318
• Main Authors: Meng, Liang, Su, Yuanhao, Kong, Xiaojia, Xu, Tongle, Lan, Xiaosheng, Li, Yunfeng
• Format: Article
• Language: English
• Subjects: Attention mechanism; Fault diagnosis; Feature augmentation; Gearbox; Residual network
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.112318

•The PFRB structure is constructed to enhance feature learning by adaptively selecting the number of PFRBs according to the data characteristics.•An attention mechanism is introduced to focus on the fault features extracted by PFRBs. Different attention values are assigned to the identified fault features and noise. Due to the difficulty of fault feature extraction and low accuracy of pattern recognition in fault diagnosis of gearboxes, a differential continuous wavelet transform-parallel multi-block fusion residual network fault diagnosis method is proposed. The signal is subjected to continuous wavelet transform after the first-order difference, which can effectively improve the resolution of the time–frequency feature images. The parallel fusion residual block (PFRB) is constructed, and the number of PFRBs can be selected adaptively based on the data features, thus enhancing the learning capability of the features. An attentional feature fusion layer is designed. This layer locates the fault features extracted by the previous layer through the attention mechanism. Through the feature fusion mechanism, the effective fault information is fused to achieve feature augmentation inside the network. The experimental results show that the proposed method has superior diagnostic performance compared with other methods in bearing and gearbox gear faults.