Abstract
The fault diagnosis method based on neural network has many defects, such as complicated network, long training time and slow convergence speed. The particle swarm optimization and neural network integration fault diagnosis methods are proposed to improve the fault diagnosis capability. Firstly, the heuristic global optimization capability of particle swarm optimization is used to optimize the neural network connection weights; then the transformer fault samples are trained and tested by using non-linear processing capacity of neural network. The test results show that such algorithm can effectively avoid unstable neural network, easily falling into local minimum and lower diagnostic accuracy etc. and can effectively increase the convergence speed and fault diagnosis efficiency compared with traditional fault diagnosis method.
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51407189: Research on The Node Effect of Integration of Three-Phase Four-Wire 400 Hz Solid-State Power Supply. 51407191: Research on An Innovative Hybrid Energy Storage Technique of Rail-type Electromagnetic Launch.
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Zhu, W., Wei, Y. & Xiao, H. Fault diagnosis of neural network classified signal fractal feature based on SVM. Cluster Comput 22 (Suppl 2), 4249–4254 (2019). https://doi.org/10.1007/s10586-018-1795-x
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DOI: https://doi.org/10.1007/s10586-018-1795-x