A Decision-Making Method for Machinery Abnormalities Based on Neural Network Prediction and Bayesian Hypothesis Testing
Open Access
- 6 July 2021
- journal article
- research article
- Published by MDPI AG in Electronics
- Vol. 10 (14), 1610
- https://doi.org/10.3390/electronics10141610
Abstract
For anomaly identification of predicted data in machinery condition monitoring, traditional threshold methods have problems during residual testing. It is difficult to make decisions when the residuals are close to the threshold and fluctuate. This paper proposes a Bayesian dynamic thresholding method that combines Bayesian inference with neural network signal prediction. The method makes full use of historical prior data to build an anomaly identification and warning model applicable under single variable or multidimensional variables. A long short-term memory signal prediction model is established, and then a Bayesian hypothesis testing-based anomaly identification strategy is presented to quantify the probability of anomaly occurrence and issue early warnings for anomalies beyond a certain probability. The model was applied to open data sets of a pumping station and actual operating data of a nuclear power turbine. The results indicate that the model successfully predicts the failure probability and failure time. The effectiveness of the proposed method is verified.Keywords
Funding Information
- National Natural Science Foundation of China (51875209)
- Open Funds of State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment (K-A2020.408, 2019B1515120060)
- Science and Technology Planning Project of Guangdong Province (2021A0505030005)
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