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| Fault diagnosis method of reactor based on a dual-channel feature fusion network with an acoustic signal |
| DOI：10.19783/j.cnki.pspc.240363 |
| Key Words:reactor acoustic signal fault features |
| Author Name | Affiliation | | SUN Kang1 | 1. School of Electrical and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2. Jiaozuo Guangyuan Electric Power Group Co., Ltd., Jiaozuo 454150, China | | ZHANG Hao1 | 1. School of Electrical and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2. Jiaozuo Guangyuan Electric Power Group Co., Ltd., Jiaozuo 454150, China | | YANG Lin2 | 1. School of Electrical and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2. Jiaozuo Guangyuan Electric Power Group Co., Ltd., Jiaozuo 454150, China | | CHANG Liang2 | 1. School of Electrical and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2. Jiaozuo Guangyuan Electric Power Group Co., Ltd., Jiaozuo 454150, China | | YANG Ming1 | 1. School of Electrical and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2. Jiaozuo Guangyuan Electric Power Group Co., Ltd., Jiaozuo 454150, China |
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| Abstract:At present, the fault diagnosis methods of a dry reactor mainly focus on mechanical faults based on vibration signals. The fault type is singular, and there are problems such as difficulties with sensor installation. Thus, a dry reactor fault test platform based on an acoustic signal is built, and a variety of fault types are set up. To improve the accuracy of fault identification in small samples, a dry reactor fault diagnosis method based on a dual-channel feature fusion network is proposed. First, the one-dimensional time sequence is converted into two-dimensional image using Gramian angle field (GAF) encoding. Secondly, a two-channel parallel CNN-ResNet network structure is adopted, and an efficient channel attention (ECA) mechanism is introduced to obtain two-dimensional key information. Then the two-dimensional image features and one-dimensional time series features are extracted and fused. Finally, the source domain data is obtained based on finite element simulation, and the optimal network parameters of the target domain are obtained by the transfer learning method. Experimental comparison shows that the proposed method has stronger feature extraction ability than other methods, can significantly separate fault features, and the fault identification accuracy can reach 99.5% with small samples. It has good generalizability and convergence speed. |
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