Probabilistic fault prediction of incipient fault

Zhao, Zhen; Wang, Fuli; Jia, Mingxing; Wang, Shu

doi:10.1109/ccdc.2010.5498474

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…In the on‐line prediction stage, after the cumulative of CI , the BAR model is used to predict the probabilistic distribution of the transformed CI in a future batch (for more details, the predictive probabilistic distribution for a continuous process can be seen in ). In the BAR model, the predictive probabilistic distribution is indicated by many samples in the future batch that obey the predictive probabilistic distribution.…”

Section: Prediction Of Non‐periodic Incipient Faultmentioning

confidence: 99%

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Zhao¹,

Zhang²,

Wang³

et al. 2014

Asian Journal of Control

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This paper is concerned with fault prediction and maintenance for the hydraulic tube tester according to the process variables. For the two types of incipient faults of this machine, it is more difficult to predict the non-periodic incipient fault and make a predictive maintenance decision in comparison with the periodic incipient fault. For the non-periodic incipient fault, a new probabilistic fault prediction method is proposed using a multivariate principle component analysis and Bayesian auto-regression model. Then, a new predictive maintenance policy is developed, based on the results of the probabilistic fault prediction approach, which implies the evolvement of system deterioration caused by the non-periodic incipient fault. Simulation results demonstrate the effectiveness of the method proposed in this paper.

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Section: Prediction Of Non‐periodic Incipient Faultmentioning

confidence: 99%

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Zhao¹,

Zhang²,

Wang³

et al. 2014

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…At this point, it is not hard to envision leveraging these data to determine failure windows of process control system components and the best time intervals to schedule preventive process control system maintenance. In terms of the determination of the time of the incipient fault, existing probabilistic prediction methods mainly include methods based on Markov and Bayesian analysis . In detail, multivariate systems can be monitored by building a principal component analysis (PCA) model using historical data.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. , one‐step prediction fault probabilities are estimated by kernel density estimation method according to the statistics corresponding control limits. While these methods of fault determination are used with reactive fault‐tolerant control, they could be also used to get an estimate of a time window where a control actuator will likely fail to be used in a proactive fault‐tolerant control scheme.…”

Section: Introductionmentioning

confidence: 99%

Proactive fault‐tolerant model predictive control

2013

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in Wiley Online Library (wileyonlinelibrary.com) Fault-tolerant control methods have been extensively researched over the last 10 years in the context of chemical process control applications, and provide a natural framework for integrating process monitoring and control aspects in a way that not only fault detection and isolation but also control system reconfiguration is achieved in the event of a process or actuator fault. But almost all the efforts are focused on the reactive fault-tolerant control. As another way for fault-tolerant control, proactive fault-tolerant control has been a popular topic in the communication systems and aerospace control systems communities for the last 10 years. At this point, no work has been done on proactive fault-tolerant control within the context of chemical process control. Motivated by this, a proactive fault-tolerant Lyapunov-based model predictive controller (LMPC) that can effectively deal with an incipient control actuator fault is proposed. This approach to proactive fault-tolerant control combines the unique stability and robustness properties of LMPC as well as explicitly accounting for incipient control actuator faults in the formulation of the MPC. Our theoretical results are applied to a chemical process example, and different scenaria were simulated to demonstrate that the proposed proactive fault-tolerant model predictive control method can achieve practical stability and efficiently deal with a control actuator fault.

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“…At this point, it is not hard to envision leveraging this data to determine failure windows of process control system components and the best time intervals to schedule preventive process control system maintenance. In terms of the determination of the time of the incipient fault, existing probabilistic prediction methods mainly include methods based on Markov [19] and Bayesian analysis [23].…”

Section: Introductionmentioning

confidence: 99%

Proactive fault-tolerant model predictive control: Concept and application

Lao

Ellis

Christofides

2013

2013 American Control Conference

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Fault-tolerant control methods have been extensively researched over the last ten years in the context of chemical process applications and provide a natural framework for integrating process monitoring and control aspects in a way that not only fault detection and isolation but also control system reconfiguration is achieved in the event of a process or actuator fault. But almost all the efforts are focused on the reactive fault tolerant control. As another way for fault tolerant control, proactive fault-tolerant control has been a popular topic in the communication systems and aerospace control systems communities for the last 10 years. At this point, no work has been done on proactive fault-tolerant control within the context of chemical process control. In this paper, we propose a proactive fault-tolerant Lyapunov-based model predictive controller (LMPC) that can effectively deal with an incipient control actuator fault. This approach to proactive fault-tolerant control combines the unique stability and robustness properties of LMPC as well as explicitly accounting for incipient control actuator faults in the formulation of the MPC. We apply our theoretical results to a chemical process example.

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Probabilistic fault prediction of incipient fault

Cited by 6 publications

References 13 publications

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Proactive fault‐tolerant model predictive control

Proactive fault-tolerant model predictive control: Concept and application

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