Tuning-Free Bayesian Estimation Algorithms for Faulty Sensor Signals in State-Space

Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2023-01, Vol.70 (1), p.921-929
Main Authors: Zhao, Shunyi, Li, Ke, Ahn, Choon Ki, Huang, Biao, Liu, Fei
Format: Article
Language:English
Subjects:
Algorithms
Bayes methods
Bayesian analysis
Bayesian estimation
Estimation
faulty signal estimation
Gaussian distribution
Heuristic algorithms
Iterative methods
Mathematical models
Noise measurement
Normal distribution
Parameters
Sensors
Signal processing
Tuning
variational inference
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Summary:Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal Bayesian estimation method is first derived by incorporating a Gaussian distribution specifying potential unmodeled dynamics into the measurement equation. Since its performance depends on tuning parameters, an iterative Bayesian estimation algorithm is developed using the variational inference technique. Specifically, an inverse Wishart distribution is introduced to describe the predicted covariance of abnormal signals. We then estimate it together with the other independent Gaussian distributions to conditionally approximate the joint posterior distribution, by which the effects of tuning parameters can be replaced adaptively. Testing the proposed algorithms through a simulated electromechanical brake model and a real experimental system shows that the proposed algorithm can satisfactorily estimate additive sensor faults online and services as a sensor monitor that simultaneously provides the locations and magnitudes of faulty signals without tuning.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3153814