Research on Voiceprint Diagnosis of Reactor Faults Based on Multivariate Perception Information Fusion
To improve the accuracy of reactor fault diagnosis,a diagnostic method based on multi perception information fusion is proposed.The method improves the LSTM(Long Short Term Memory)network by using the PSO(Particle Swarm Optimization)algorithm to optimize the number of neurons and dropout values,and uses the improved LSTM network to classify and recognize the voiceprint of reactor faults,achieving reactor fault diagnosis.The simulation results show that using PSO algorithm to improve LSTM network can improve the convergence speed and classification recognition accuracy of LSTM network;The PSO algorithm is used to improve the LSTM network model for classifying and identifying reactor fault voiceprints,which can effectively diagnose different types of reactor faults such as rated pre tension looseness,unilateral clamp looseness,and bilateral clamp looseness,and has a high accuracy rate,with an average accuracy of about 98%;Compared to SVM,CNN,BP models,and standard LSTM network models,the LSTM network model improved by the PSO algorithm has a higher accuracy in voiceprint diagnosis of reactor faults and significant advantages.
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