State identification of large diameter wet steam pipeline in nuclear power conventional island

Abstract

The safety of nuclear power plant is an inevitable condition for maintaining the long-term and stable development of nuclear power. Pipeline vibration is one of the common causes of power plant operation faults. Therefore, the state identification of pipeline vibration is very important. Taking a nuclear power heating pipeline as an example, the original vibration signal is decomposed and reconstructed by Wavelet Packet Transform, and the energy eigenvectors in X, Y and Z directions are established. In order to analyze the state of pipeline more accurately, two recognition algorithms of support vector machine (SVM) and SOM neural network are proposed. By comparing the classification effect and classification accuracy of the SVM before and after optimization, the optimized SVM and SOM neural network, it is concluded that the optimized SVM has better effect on pipeline state recognition, and the effectiveness of this method on pipeline state recognition is verified.