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.
Highlights
With the rapid development of machine learning, especially deep learning, various models based on data-driven algorithms capable of predicting future data using historical data have been developed and are widely used in the field of signal prediction
Further research will be carried out in the following three aspects: (1) processing anomaly identification for time-series data with multiple variables, (2) making full use of the a priori information for decision-making, which is more in line with the actual application situation, and (3) calculating the confidence level for the identified anomalies and providing early warning based on the quantified probability
Probabilistic principal component analysis (PPCA) [32] is employed to reduce the dimensionality and uncertainty of the a priori data, which is based on the Gaussian latent variable model by introducing a probabilistic framework into principal component analysis to attenuate the influence of noisy variables on the structural characteristics of the data
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Persio and Honchar [12] propose a novel approach to energy load time series forecasting, combining deep learning networks with the prior properties of Bayes to predict the data of interest He et al [13] used deep confidence network to carry out unsupervised fault diagnosis for gear transmission chains. Further research will be carried out in the following three aspects: (1) processing anomaly identification for time-series data with multiple variables, (2) making full use of the a priori information for decision-making, which is more in line with the actual application situation, and (3) calculating the confidence level for the identified anomalies and providing early warning based on the quantified probability.
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