## 7 Conclusions  
In this paper, a CNN-based method was proposed for diagnosing the faults of reciprocating compressors based on single-measuring point vibration signal or multi-measuring points' signal, including vibration, pressure, and key phase signal. In single-point vibration signal, the 1-D CNN model in the deep learning method was used for diagnosis and comparison with three typical methods. The recognition accuracy of 1-D CNN was 100%, which is higher than the other three typical models compared. The results demonstrate the effectiveness of the 1-D CNN model. Next, in order to compare the accuracy of the single-point model and multi-point model, experiments based on 1-D CNN were done to validate it. The experimental results showed that the accuracy of the 1-D CNN single-point  
Figure 16: Comparison of the diagnosis effect under different numbers of measuring points.  
Figure 17: Comparison of the fusion diagnosis effect of different measuring points when the number of measuring points is 7.  
model is higher than the 1-D CNN multi-point model under the 5 SNRs. When the SNR was 10 dB, the difference was the largest and the accuracy of the 1-D CNN multi-point model was only 55.59%. However, the accuracy of the 1-D CNN single-point model was 85.58%. Then, a 2-D CNN multi-point diagnosis model was established and compared with the previous two models. The results showed that the diagnostic accuracy of the 2-D CNN multi-measuring point model was slightly lower than the previous two models when the SNR was higher (20 dB, 50 dB, no noise). However, the accuracy was significantly higher than the previous two models when the SNR ratio was 10 dB. When the signal-to-noise ratio was 5 dB, the accuracy of the three models was relatively low, less than 60%. This is enough to prove the effectiveness of the 2-D CNN multi-measuring point model, but the performance of the model needs to be improved. Therefore, the influence of the number of measurement points and the type of measurement points were studied on the diagnosis results under several original signals with different SNRs and it was found that when the number of measurement points was 7 (the key phase signal was deleted), the diagnostic accuracy was the highest(10 dB, 5 dB) in the different measuring points' layout. It was proven that the proper selection and reduction of the measuring point signal can improve the efficiency of diagnosis while ensuring a higher diagnosis accuracy.  
Formal analysis, S.X., A.N. and S.L.; Investigation, S.X., M.S. and H.Z.; Funding acquisition, S.L., S.X. and M.S.; Methodology, A.N. and S.X.; Software, A.N. and H.Z.; Supervision, M.S. and H.Z.; Validation, A.N.; Writing--original draft, Z.Z.; Writing--review and editing, S.X. and S.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of China (Grant Nos. 51575331), Young teacher special project of Ningde Normal University (Grant No. 2018 ZX401, No. 2019 ZX401, No. 2019 ZX403) and Major Projects of Industry, Education and Research in Universities of Fujian Province (Grant No. 2017H6018). These supports are gratefully acknowledged.  
The authors declare no conflict of interest.