# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 6 Conclusions  
This paper focuses on rotating machinery and proposes a fault diagnosis method for rotating machinery based on edge computing. Firstly, an edge node is designed that integrates signal acquisition, data preprocessing, feature extraction, and fault diagnosis classification. Secondly, a fault diagnosis method based on dual-channel CNN decision-level fusion was proposed, which was optimized in three aspects to enhance its performance in motor fault diagnosis: (1) a dual-input channel design was employed, combining temperature and vibration sensors for fault analysis and diagnosis. Separate CNNs were constructed for each data modality, and the decision probabilities output by each CNN were then fused. This eliminates the requirement for consistency in data sampling during feature fusion in the CNN model, allowing fusion even when the data sampling frequencies are not the same. It also avoids issues such as low recognition rates, lack of evidence, and data redundancy in equipment fault diagnosis when using a single sensor under complex operating conditions. (2) In the vibration sensor channel, the envelope spectrum is used as a substitute for the raw measurement input. (3) The D-S evidence theory method is incorporated into the fully connected layer to perform decision-level fusion of the dual-input CNNs, which significantly enhances the model's robustness, generalization ability, and performance. After the modifications, three metrics (average accuracy, rapidity, and stability) were defined to evaluate the overall performance of the optimized algorithm. The experimental data validation demonstrates that the proposed optimizations can improve the algorithm's performance to varying degrees. Finally, the data preprocessing and the improved CNN-DS model were deployed on the edge node, and its effectiveness was validated, achieving fault diagnosis for rotating machinery based on edge computing.  
Conceptualization, Z.W. and M.C.; methodology, M.C.; validation, M.C.; formal analysis, C.G.; investigation, Q.Y.; resources, Q.Y.; data curation, C.G.; writing--original draft preparation, M.C.; writing--review and editing, M.C.; supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
The support of this work by the National Natural Science Foundation of China (No. 51975386), Liaoning Province \"Unveiling and Commanding\" technology projects (2022020630-JH1/108).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. (The datasets generated during and/or analysed during the current study are not publicly available due to Information related to enterprise product processing).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON] and [PERSON] are employees of Ansteel Group Automation Co., Ltd., but the company did not provide funding or technical support for this research. The two funding projects mentioned in the paper were all awarded to Professor [PERSON], and the funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.