## 1 Introduction  
In recent years, intelligent operation and maintenance and health management technologies have been presented in the field of mechanical systems from unique perspectives. In the literature, fault diagnosis methods based on signal processing [1, 2, 3] and physical modeling [4, 5] have gradually matured. However, traditional diagnostic methods require a high level of human a priori knowledge, which limits their application and diffusion. Commonly, the dynamic response of a system, due to a change of state, is reflected in the sensor measurements. By monitoring the consistency between these measurements and the machine operational regime, it is possible to predict the operating status of the machine and any potential faults [6]. Therefore, data-driven fault diagnosis techniques based on data have become a hot issue for current research. Deep neural networks (DNNs) are well suited for data-driven fault diagnosis methods, due to their robust learning and feature representation capabilities. Models such as Convolutional Neural Networks (CNN) [7], Generative Adversarial Networks (GAN) [8], Autoencoders (AE) [9], Long Short-Term Memory Neural Networks (LSTM) [10] and their variants are widely used for mechanical equipment fault prediction and identification. Meanwhile, the cutting-edge techniques of computer vision (CV) and natural language processing (NLP) are widely used in the field of fault diagnosis, in processes such as active learning [11] and transfer learning [12]. It is worth mentioning that skip connection in residual networks (ResNet) [13] allows for the increased complexity of the diagnostic model without the loss of performance. Therefore, ResNet is widely used in fault diagnosis studies [14, 15, 16, 17, 18]. All the above-proposed methods have high diagnostic accuracy and generalization capability. However, the DNN model training process is an \"end-to-end\"automated learning approach. This pattern makes the DNN model an elusive \"black box\" model, and the intermediate process of the DNN model is no longer transparent to the user. Obviously, users prefer models that can be interpreted. From the perspective of control theory, DNN becomes an uncontrolled system. Encouraged by the current research on deep learning interpretability, interpretability research regarding deep learning fault diagnosis is viewed as meaningful work. If the objective laws of the DNN learning of typical fault features can be mastered, it is possible to control the learning and optimization of the model from the model output, increasing the reliability and mathematical theoretically of the model. Some researchers [19; 20] exploratively combined deep learning method approaches with traditional interpretable methods to further enhance the theoretical nature of deep networks. Inevitably, changing the inputs to the system alone does not allow for understanding and controlling the trajectory of change in the system's internal behavior. Redundant preprocessing techniques may even cause the loss of original features and undermine the adaptive learning capability of deep learning models. [PERSON] et al. [21] proposed an interpretable learning method based on CNN and the fuzzy C-mean (FCM) clustering algorithm to achieve the fault diagnosis of rolling bearings. [PERSON] et al. [22] propose a collaborative deep learning framework that conveys features as a representation of the data through the potential parameters of the deep learning structure. [PERSON] et al. [23] used Layer-wise Relevance Propagation (LRP) as a metric, quantifying the contribution of the nonlinear output and giving interpretable diagnostic results from the perspective of correlation consistency. [PERSON] et al. [24] used decoupling operators to solve the intra-class variance problem and achieved feature separation during the training process. The articles [25; 26; 27; 28] also proposed different interpretable deep learning networks.