AUTHOR=Yin Yuan , Xu Fenqin , Pang Bo 

TITLE=Online intelligent fault diagnosis of redundant sensors in PWR based on artificial neural network

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1011362

DOI=10.3389/fenrg.2022.1011362

ISSN=2296-598X

ABSTRACT=Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structure to improve system safety and reliability. However, reliability of the actual system is often lower than that obtained by theoretical calculation due to the inevitable occurrence of common mode fault (CMF), which is a dependent failure event that can cause multiple failures in the redundant channels. CMF may increase the reliability deviation of the system by orders of magnitude and hence seriously affects the reliability of the system. To mitigate CMF of redundant sensors in nuclear power plants, artificial neural network (ANN) can serve as a data-driven analytic model to monitor sensor parameters, to identify possible abnormal status of the sensors and provide an early warning. In this study, by using the high-fidelity dataset obtained in a full-scope PWR simulator as training, validation and test data, a relevant parameter-based ANN black-box model (RPANN) were established by employing the back propagation (BP) learning algorithm, which is then defined as an analytic redundancy. Time series-based ANN checking models (TSANNs) were also established for each of the input and output parameters of RPANN, in order to identify its abnormal state based on historical data in the past. When combined with the existing hardware redundancy, the ANN-based analytic redundancy can serve as an online monitoring of the hardware status and an online diagnosis of sensor faults. Furthermore, ANN-based analytic redundancy can replace faulty hardware sensors to analytically reconstruct reading of the monitored sensor parameter without having to reducing the reactor output power or even shutting down the reactor for emergence maintenance, so that on-site calibration frequency of hardware sensors in redundant channels can be effectively reduced. This is not only of vital importance in reducing operation and maintenance costs of existing PWR power plants, but plays also an important role in building reactor operation schemes with rapid and frequent changes of power output in the future. Simultaneously, the diverse redundancy combining analytic software redundancy and physical hardware redundancy can effectively reduce the threat of CMF of hardware sensors on the operation safety of the reactor systems.