[13] developed an automatic algorithm capable of identifying any abnormal mechanical behavior captured by vibration measurements. [PERSON] et al. [14] proposed a RUL prediction model based on the auto-encoder gated recursive unit (AE-GRU), in which the auto-encoder (AE) extracted important features from the original data and the gated recurrent unit (GRU) selected information from the sequence to predict RUL. [PERSON] et al. [15] proposed an innovative algorithm that combines a hybrid spatial and temporal attention-based gated recursive unit (HSTA-GRU) with the seasonal trend decomposition program Loess (STL) to predict more fault information from multiple time series data. Based on the graph convolutional network (GCN) and GRU models, [PERSON] et al. [16] proposed a new GCG framework combining a GCN and GRU to extract features and predict shaft temperature. [PERSON] et al. [17] developed a hybrid prediction method for mechanical degradation. In this method, an algorithm based on 3r criteria was introduced to detect the initial time point of degradation, and the GRU network was used to learn the degradation characteristics based on existing data, to predict the long-term degradation trend through multiple prediction programs. At present, how to quickly and accurately identify the faults generated during the operation of equipment has become a research hotspot in the prediction and maintenance management of mechanical equipment health status [18]. To diagnose equipment faults timely and accurately and maintain the normal operation of the equipment, a variety of intelligent diagnosis methods have been proposed, mainly including signal processing methods [19] and data-driven methods [20]. With the support of an intelligent data acquisition system to obtain a large amount of original available data, data-driven fault diagnosis technology has gradually entered the research field [21; 22]. General intelligent diagnosis and prediction methods are mainly composed of two parts, namely feature extraction and fault classification [23]. At present, many machine learning methods have been applied to mechanical fault diagnosis such as the artificial neural network (ANN) [24], SVM [25], hidden Markov model (HMM) [26], and so on. Different from traditional machine learning methods that adopt supervised learning, unsupervised-learning-based deep learning can realize fault diagnosis when samples are scarce, providing an effective solution for fault feature diagnosis and analysis. This advantage makes unsupervised feature learning methods gradually enter the field of mechanical fault diagnosis [27; 28]. [PERSON] et al. [29], for example, proposed a hybrid flexible diagnosis framework for rolling bearings based on a DBN model as a reliable and effective general method for bearing fault diagnosis. [PERSON] et al. [30] proposed a fault diagnosis method based on a sparse auto-encoder (SAE) for advanced feature learning and bearing fault classification, which improved diagnosis accuracy and efficiency. [PERSON] et al. [31] proposed a partial adversarial domain adaptive (SPADA) model based on stacked auto-encoders to solve the fault diagnosis problem in PDA, and the diagnostic performance of SPADA is superior to existing methods. In the field of unsupervised learning, the self-organizing mapping (SOM) neural network is a popular algorithm in the field of data clustering, and it is often used in the field of fault diagnosis because of its excellent effect of cluster analysis. [PERSON] et al. [32] proposed a gear fault intelligent diagnosis model based on the SOM neural network, which can predict the remaining service life of gear transmission systems according to state indicators. [PERSON] et al. [33] proposed a method to diagnose the converter fault of wind turbines by using the SOM method, which avoids training many samples and has high accuracy. [PERSON] et al.