Knowledge base operator support system for nuclear power plant fault diagnosis

A high-tech, high-performance system such as the nuclear power plant needs a wide range of support for operators to efficiently operate the plant, interpret and manage the volume of information available, and detect and diagnose fault in a timely manner. Increasingly, application of artificial neural networks and its variants for fault detection and isolation has moved from toy examples to real-world systems. However, different network architectures respond to different data set in different ways, and the complex, dynamic, high background noise, overlapping patterns and non-linear characteristics of Nuclear Power Plants (NPP) requires a careful selection of a suitable neural network architecture that reflects these traits. This work presents a pilot scheme towards the development of a comprehensive knowledge base for the operator support system of the Chinese Qinshan II NPP, using Principal Component Analysis (PCA) and artificial neural networks. In this work, we utilize the PCA method for noise filtering in the pre-diagnostic stage, and evaluate the predictive/regression capability of two different recurrent neural networks - The Elman neural network and the Radial Basis Network - on a representative data from Qinshan II NPP. The process was validated using data from different fault scenarios simulated on a desktop Pressurized Water Reactor simulator, and the fault signatures were used as the input. The predictive outputs required are the location and sizes of the faults. The result shows that the Radial Basis network gives better prediction and diagnoses the faults faster than Elman neural network. Some of the important diagnostic results obtained from the networks are presented in this paper, and they serve as the pilot study for the development of knowledge base for the computerized NPP operator support system.