Ensemble Multiple Distinct ResNet Networks With Channel-Attention Mechanism for Multisensor Fault Diagnosis of Hydraulic Systems

Hydraulic systems are widely used in modern industries, and the study of efficient and accurate fault diagnosis techniques for hydraulic systems can help prevent accidents and reduce economic losses. Most of the existing deep learning-based intelligent diagnosis methods use only single-channel signals for diagnosis, which may ignore some important fault information. In addition, the existing deep learning algorithms cannot distinguish the sensitivity of interchannel features, and the important features of the channel domain cannot be emphasized. To solve the above problems, this article proposes a multichannel data-driven framework integrated distinct ResNet networks with channel-attention mechanism (CM-ResNet) for fault diagnosis of hydraulic systems. First, the multichannel signals are converted into 2-D feature maps by continuous wavelet transform (CWT). Then, multiple CM-ResNet is trained as base learners based on the signals of each channel. Third, the category probabilities of each base learner are concatenated into new feature vectors. Finally, the new feature vectors are used to train the meta-learner and perform fault diagnosis. The meta-learner can capture the complex nonlinear relationships between the base learners and obtain a stronger learner. Experimental results show that the performance of our proposed method outperforms the comparison method and the already existing methods.