A Novel Principal Component Analysis-Informer Model for Fault Prediction of Nuclear Valves
An Zhao, Lan Cheng, Yuanjun Guo, Mifeng Ren, Wei Feng, Bo Sun, Jun Ling, Huanlin Chen, Weihua Chen, Yalin Luo, Zhile Yang
Abstract
In this paper, a deep learning fault detection and prediction framework combining principal component analysis (PCA) and Informer is proposed to solve the problem of online monitoring of nuclear power valves which is hard to implement. More specifically, PCA plays the role of dimensionality reduction and fault feature extraction. It maps data with multi-dimensional space to low-dimensional space and extracts the main features. At the same time, the T-square and Q statistic thresholds are also provided to realize abnormal status monitoring. Meanwhile, Informer is a long-term series prediction method. It encrypts and decrypts data through the encoder and decoder to train a prediction model. Through the training of fault data, fault prediction can be realized. Experiments based on the sound waves collected from real valves can be continued, which also illustrates the effectiveness of the PCA–Informer model for fault diagnosis and fault prediction of nuclear power valves. Therefore, the online monitoring and maintenance of nuclear valves and other important equipment, without shutting down the nuclear station, can be achieved.