## 1 Introduction  
With the coming of the IoT (Internet of Things) era, the reliability of mechanical equipment is more and more demanding [1]. The rotating components of mechanical equipment such as shafts, bearings, and gears are prone to shaft cracks, bearing wear, and gear breakage, which have great safety hazards [2]. In order to ensure the safe and reliable operation of equipment, accurate and efficient fault diagnosis technology is particularly important. As a powerful tool for processing big data, the deep learning [3, 4] model has been successfully applied to mechanical intelligent fault diagnosis. In the field of intelligent fault diagnosis of rotating parts, many scholars have studied related diagnosis by using various deep learning methods. [PERSON] et al. [5] used the features extracted from the time domain, frequency domain, and time-frequency domain of vibration signals as deep Boltzmann machine (DBM) inputs to realize the accurate classification of seven different bearing faults. [PERSON] et al. [6] adopted the wavelet packet energy map as the input signal and designed the depth convolutional neural networks (CNN) for bearing fault diagnosis. Meanwhile, in order to better display the multi-layer representation of the neural network, a multi-scale layer was added behind the final convolutional layer as the connection between the output of the final convolutional layer and the previous pooling layer. [PERSON] et al. [7] first implemented wavelet packet transform (WPT) on the original vibration signal, and then input the extracted statistical characteristics into a two-layer DBMto realize the fault diagnosis of the gear box, and the classification rate of 11 operating modes reached 97.68%. [PERSON] et al. [8] proposed a method based on variational mode decomposition (VMD) and CNN for feature extraction and fault diagnosis of planetary gear for local weak feature information, and realized the identification and classification of planetary gear weak fault state, where VMD based partition extraction method was better than EEMD, which could achieve 100% total CNN recognition rate with less training time (14 times). [PERSON] et al. [9] not only adopted the statistical features in the time domain and frequency domain, but also added the test values of load and speed to form a set of feature vectors as the input of the deep belief network (DBN), realizing the fault diagnosis of the gear box. Even more exciting, stacked auto encoders (SAE) [10], as a widely used deep learning algorithm, has attracted attention in the field of fault diagnosis. [PERSON] et al. [11] established a five-layer SAE and used frequency domain signals as input to diagnose the rotating bearing faults under different loads and speeds, with the diagnosis accuracy reaching almost 100%. [PERSON] et al. [12] proposed an unsupervised feature learning method: sparse filtering (SF). This algorithm only focuses on optimizing the sparse performance of learning features while ignoring the distribution of learning data. [PERSON] et al. [13] introduced sparse filtering into mechanical intelligent fault diagnosis. First, sparse filtering was used to extract unsupervised features of the time-domain signals of bearings, and then softmax was used as a classifier to realize fault diagnosis and good results were obtained. For various fault diagnosis components, the main intelligent diagnosis algorithm was to detect the fault of the bearing and gear, respectively. [PERSON] et al. [14] selected the method of adding Gaussian noise to the training data for data enhancement, and then input it into SAE for bearing fault diagnosis. [PERSON] et al. [15] obtained spectrum samples by short-time Fourier transform (STFT) operation of the bearing vibration signals, and then input them into CNN for network training. The results showed that this method had high identification accuracy for different bearing fault types and could improve the robustness of this method by increasing the type and number of fault samples. [PERSON] et al.