A mixed intelligent condition monitoring method for nuclear power plant

Abstract

Nuclear power plant is a highly safety required system which has multi- operating condition in different power mode, and it requires a more advanced technology to realize condition monitoring. To improve the condition monitoring techniques, a mixed condition monitoring method based on sparse autoencoder and isolation forest is proposed to realize the condition monitoring of nuclear power plant, where sparse autoencoder is responsible for data feature extraction and dimensionality reduction, and isolation forest is responsible for the anomaly monitoring of nuclear power plant. The proposed method can transform high-dimensional data into a low-dimensional space, remove the redundancy of the data, and then identify the state through a high-performance monitoring model, thereby improving monitoring efficiency and accuracy. In order to expound the performance of the condition monitoring model proposed in this paper, we select one operating condition and two operating conditions for testing. We also obtained the condition monitoring results of local outlier factor and one-class support vector machine to compare with our method. From the results, it can be known that sparse autoencoder can extract the nature of operating data, and monitoring accuracy of 100% and 98% can be achieved under one operating condition and two operating conditions by isolation forest method, respectively. Compared with other methods, the proposed method has obvious advantages. This research has important implications for the condition monitoring of nuclear power plant and the system with multi-operating conditions.