Weighted average selective ensemble strategy of deep convolutional models based on grey wolf optimizer and its application in rotating machinery fault diagnosis

To improve the fault diagnosis performance of rotating machinery under harsh conditions, a weighted average selective ensemble strategy of deep convolutional models based on the grey wolf optimizer (GWO) is proposed. Firstly, two datasets in the time domain, two in the frequency domain, and one in the time-frequency domain are respectively constructed to guarantee the diversity and comprehensiveness of input expression. Secondly, two deeper Resnet18 models, which are sensitive to the comprehensive and abstract features, and two relatively shallow CNN models, which focus on the details more, are built. Thus, the fault differential features are diverse. Moreover, the improved convolutional block attention module (CBAM) is used to enhance the diagnosis performance. Then, a total of ten individual models are trained. Thirdly, F1 Score is used to evaluate the diagnostic performance of each individual model on different faults, and the fault class-specific thresholds are set. For each fault, individual models with F1 Score lower than the corresponding thresholds are regarded as negative and need to be discarded. Especially, the fault class-specific thresholds are optimized by GWO. Finally, the weighted average selectively ensemble strategy is implemented based on the threshold-treated weights. Experiment results indicate that the proposed ensemble model significantly improves the diagnostic accuracy and stability of the individual models, which is also verified by other compared ensemble strategies and ensemble models.