Improved fault diagnosis method of electric gate valve in nuclear power plant

Abstract

The vibration signal (VS) analysis and acoustic emission signal (AES) analysis methods are the common effective diagnostic methods in valve fault diagnosis (FD)(Classify fault types). However, these two methods are disadvantaged by the surrounding environment and noise signals produced during acquisition. To obtain an improved FD effect of an electric gate valve (EGV) in nuclear power plant, the two previous methods are ameliorated by a noise reduction processing, then the signal feature extraction is performed. The FD and degree evaluation are carried out for the common fault types of the EGV taken as object. In this paper, three-phase unbalanced faults (Acquisition of VS), packing damage faults (Acquisition of VS) and internal leakage faults (Acquisition of AES) are taken as examples. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm composed with Fast Fourier Transform (FFT) and the SAEU3H series digital acoustic emission detection system (SAEU3H SDAEDS) are used to noise reduction and extraction of parameters of the VS and AES, respectively. Then, the extracted feature parameters are brought into the Bi-directional Long Short-Term Memory (Bi-LSTM) deep neural network for FD and fault degree evaluation (Assess the severity of failure occurrences). The experimental results show that the method adopted in this paper has high FD accuracy and low fault evaluation error.