Abstract
Although the state evaluation method based on characteristic parameters and weight factors can extract the characteristic quantities in time domain and frequency domain according to the collected acoustic and vibration signals of reactors, it is necessary to analyze a large number of test data to establish the functional relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states The method can directly learn the data samples, and self-study the correlation rules of characteristic parameters and defects through the training of neural network. In this paper, the deep learning neural network model is constructed, and the data obtained from reactor defect simulation experiment and field measurement are used as samples to train the deep learning network. Through the training of neural network, the characteristics of acoustic vibration signal are automatically learned, and the characteristics are stored in the parameters of neural network. Finally, the state of reactor is realized by the classifier at the end of the network assessment
Highlights
For acoustic signal and vibration signal, the common fault diagnosis methods are based on analytical model, signal processing and artificial intelligence
The fault diagnosis method based on signal analysis is to use the signal analysis theory to obtain a variety of deep-seated eigenvectors in time domain and frequency domain, and use the relationship between these eigenvectors and system fault sources to determine the location of fault sources[4]
The state evaluation method based on characteristic parameters and weight factors can extract the characteristic quantities in time domain and frequency domain according to the collected acoustic and vibration signals of reactors, it is necessary to analyze a large number of test data to establish the functional relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states The method can directly learn the data samples, and self-study the correlation rules of characteristic parameters and defects through the training of neural network
Summary
For acoustic signal and vibration signal, the common fault diagnosis methods are based on analytical model, signal processing and artificial intelligence. The fault diagnosis method based on signal analysis is to use the signal analysis theory to obtain a variety of deep-seated eigenvectors in time domain and frequency domain, and use the relationship between these eigenvectors and system fault sources to determine the location of fault sources[4] This method is mainly used in the system where the analytical model of the diagnosis object is difficult to establish, but some state or output parameters of the system can be measured. For the acoustic vibration detection system operating in the actual working condition, the biggest difficulty is that there is no normal operation reactor acoustic vibration data and reactor acoustic vibration data under different operating conditions, especially the reactor acoustic vibration data under defect state is relatively small In this case, the deep learning method is more suitable because it does not need a large number of training samples Use
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