Wavelet Convolutional Neural Network with Multilabel Classifier: A Compound Fault Diagnosis Framework and Its Interpretability Analysis

With the development of intelligent sensing, measurement, and data analytics technologies, exploring intelligent fault diagnosis methods has attracted enormous attention in the past decade, in which many successful attempts have been achieved for compound fault diagnosis of industrial equipment. However, the traditional intelligent compound fault diagnosis methods are pure deep learning algorithms, resulting in these methods being 'black box' solutions and cannot be interpreted. To explore a solution for the above problem, an intelligent and interpretable compound fault diagnosis method, named wavelet convolutional neural network with multilabel classifier (WavCNN-MLC), is proposed for industrial equipment. First, the WavCNN-MLC is constructed with the following steps: 1) a wavelet convolutional layer is introduced to substitute the first layer of the traditional convolutional neural network (CNN), which aims to learn features with interpretable meaning from vibration signals; 2) a multilabel classifier is employed to substitute the classifier of traditional CNN, which endows the diagnosis model with the ability to decouple the compound fault intelligently. Second, the WavCNN-MLC is trained and optimized with the single and compound fault samples in a supervised way. Finally, a variant of Gradient-weighted Class Activation Mapping, called Grad-CAM++, is applied to analyze the relationship between the target class and the key activation areas of inputs, which can provide visual explanations for the classification results of the WavCNN-MLC. The experimental results on a gearbox dataset show that the proposed method can effectively decouple the compound faults and demonstrate that the WavCNN-MLC is interpretable through the visualization of the saliency map.