PT-Informer: A Deep Learning Framework for Nuclear Steam Turbine Fault Diagnosis and Prediction

Abstract

The health status of equipment is of paramount importance during the operation of nuclear power plants. The occurrence of faults not only leads to significant economic losses but also poses risks of casualties and even major accidents, with unimaginable consequences. This paper proposed a deep learning framework called PT-Informer for fault prediction, detection, and localization in order to address the challenges of online monitoring of the operating health of nuclear steam turbines. Unlike traditional approaches that involve separate design and execution of feature extraction for fault diagnosis, classification, and prediction, PT-Informer aims to extract fault features from the raw vibration signal and perform ultra-real-time fault prediction prior to their occurrence. Specifically, the encoding and decoding structure in PT-Informer ensures the capture of temporal dependencies between input features, enabling accurate time series prediction. Subsequently, the predicted data are utilized for fault detection using PCA in the PT-Informer framework, aiming to assess the likelihood of equipment failure in the near future. In the event of potential future failures, t-SNE is utilized to project high-dimensional data into a lower-dimensional space, facilitating the identification of clusters or groups associated with different fault types or operational conditions, thereby achieving precise fault localization. Experimental results on a nuclear steam turbine rotor demonstrate that PT-Informer outperformed the traditional GRU with a 4.94% improvement in R2 performance for prediction. Furthermore, compared to the conventional model, the proposed PT-Informer enhanced the fault classification accuracy of the nuclear steam turbine rotor from 97.4% to 99.6%. Various comparative experiments provide strong evidence for the effectiveness of PT-Informer framework in the diagnosis and prediction of nuclear steam turbine.