Signal anomaly identification strategy based on Bayesian inference for nuclear power machinery

In the machinery industry, signal anomalies are generally identified using the threshold method, which exhibits shortcomings in setting reasonable thresholds, in decisionmaking when signals approach thresholds or fluctuate, and in quantification of fault confidence. In this paper, a long short-term memory (LSTM) model is established to predict the time-series signals. For prediction residual, a novel decision-making strategy of signal anomaly identification based on Bayesian inference is then proposed that considers data uncertainty. Various signal abnormality conditions are analyzed, and a Bayesian hypothesis test approach is developed to determine the signal status and quantify the fault probability. After fully mining the prior information of the residuals to reduce the influence of randomness, estimates of the key parameters, namely residual mean and variance, are determined by obtaining the posterior distribution based on the normal-inverse-gamma distribution. In two nuclear power machinery examples, all potential signal anomalies are identified by the proposed method. The results of a comparative analysis with existing methods demonstrate that the proposed method can issue an alarm several hours in advance and provide a fault probability, which improves the accuracy and reliability of prediction. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper introduces a novel method for signal anomaly identification using LSTM model for signal prediction and Bayesian inference. By analyzing various signal abnormality conditions and utilizing Bayesian hypothesis test approach to determine signal status and quantify fault probability, the proposed method demonstrates improved accuracy and reliability of prediction in comparison with existing methods.