## 5 Conclusions  
In this paper, the vibration dataset in the field of bearing fault diagnosis research is expanded by conducting a health state classification study on the XJTU-SY bearing dataset. After that, a deep learning network model is built to classify the divided three-condition XJTU-SY dataset for fault diagnosis research. The three parameters of RMS, kurtosis, and STFT SUM are introduced to construct the bearing health factor degradation index, and quantify each health factor in terms of trend, monotonicity, and robustness. After comparison, the RMS health factor is selected, and the first-order differential mutation point under the threshold condition is used as the failure degradation starting point of the bearing, which divides the whole life cycle of the bearing into two phases: the healthy phase and the fault phase. For the divided dataset of the XJTU-SY bearing under three working conditions, based on the WDCNN algorithm, this paper establishes a deep learning network model by introducing FFT signal feature extraction, a multi-scale first-layer wide convolution kernel, and a GRU recurrent neural network.  
By comparison with the other five network models (WDCNN + original vibration data, WDCNN + FFT, Propose + original vibration data, SVM + FFT, and ANN + FFT), the results show that the network model proposed in this paper has excellent fault diagnosis capability and model noise immunity, which provides important technical support for bearing fault diagnosis in actual industrial processes.  
Although the proposed method has made some achievements, there are still two limitations that need to be improved in future work. Firstly, the coverage of data in this paper is not perfect and the fault diagnosis in this paper is only for historical data due to the limited practical conditions. In the future, we can build our experimental platform to collect vibration signals. Fault diagnosis can also be extended to the scope of online detection and is not limited to the application of datasets. Secondly, in this paper, when dividing the health phase and fault phase of the bearing, the division point is taken as the  
Figure 17: Visualization of model test set classification process: (**a**) Initial test set; (**b**) initial test set; (**c**) SoftMax.  
collected minute unit. In future work, the state within minutes will be divided step by step, and some deeper theoretical models can be applied for health state division, such as the Hidden Markov Model and the Marxian distance.  
Conceptualization, L.S. and S.S.; methodology, L.S. and W.W.; validation, L.S. and S.S.; writing--original draft preparation, L.S. and S.G.; writing--review and editing, L.S.; visualization, supervision, project administration, funding acquisition, S.S., C.C. and W.W. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Natural Science Foundation of China (grant no. 51475129, 51675148, 51405117). This research was funded by the application and demonstration of \"intelligent generation\" technology for small and medium-sized enterprises--R & D and demonstration application of the chair industry internet innovation service platform based on artificial intelligence (grant no. 2020C01061).  
Not applicable.  
The author would like to thank [PERSON] for advice in writing--review and editing.  
The author of this article declares that there is no conflict of interest related to this manuscript.