Abstract
This paper proposes a sensor and actuator fault diagnosis method for small pressurized water reactors (SPWRs), with an innovative labeled fault dictionary established to map complex fault modes, using long short-term memory (LSTM) networks. It can directly learn features from multivariable time-series data and capture long-term dependencies through the cyclic behavior and gate mechanism of LSTM to realize the end-to-end fault diagnosis of SPWRs. Experimental results on a SPWR fault dataset show that the method can effectively diagnose the location, type, and extent of sensor and actuator faults from raw time-series signals with an average accuracy of 92.06% and outperforms three other widely-used fault diagnosis methods. Furthermore, the diagnosis results on the SPWR fault dataset injected with different noise signals demonstrate the strong noise immunity capability of the established LSTM network. Therefore, the proposed method is expected to achieve satisfactory fault diagnosis performances in actual operating environments of SPWRs.
Published Version
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