A Novel Degradation Modeling and Prognostic Framework for Closed-Loop Systems With Degrading Actuator

Si, Xiaosheng; Ren, Ziqiang; Hu, Xiaoxiang; Hu, Changhua; Shi, Quan

doi:10.1109/tie.2019.2952828

Cited by 38 publications

(16 citation statements)

References 29 publications

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“…So far, key maintenance technologies like condition monitoring (CM), prognostics and health management (PHM), performance degradation recovery (PDR) or fault-tolerant control (FTC) are not addressed. The interested reader is referred to [5,16,25,[60][61][62][63] and references cited therein. We would like to emphasize the two aspects of fault diagnosis and performance degradation monitoring in automatic control systems.…”

Section: Functionality-oriented Performance Degradation Monitoringmentioning

confidence: 99%

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

Ding

2022

Security and Safety

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This note addresses diagnosis and performance degradation detection issues from an integrated viewpoint of functionality maintenance and cyber security of automatic control systems. It calls for more research attention on three aspects: (i) application of control and detection unified framework to enhancing the diagnosis capability of feedback control systems, (ii) projection-based fault detection, and complementary and explainable applications of projection- and machine learning-based techniques, and (iii) system performance degradation detection that is of elemental importance for today’s automatic control systems. Some ideas and conceptual schemes are presented and illustrated by means of examples, serving as convincing arguments for research efforts in these aspects. They would contribute to the future development of capable diagnosis systems for functionality safe and cyber secure automatic control systems.

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Section: Functionality-oriented Performance Degradation Monitoringmentioning

confidence: 99%

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

Ding

2022

Security and Safety

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“…In terms of individual devices, by taking advantage of the property of full life-cycle modeling, it is expected to achieve an elevation in the prognosis performance in a multi-timescale manner [108]. Performance supervised monitoring is optimized by integrating the (short-term) monitoring of system dynamics and the (long-term) prediction of performance degradation.…”

Section: ) Auxiliary Key Technologiesmentioning

confidence: 99%

Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap

Jiang

Yin

Kaynak

2021

IEEE Open J. Ind. Electron. Soc.

114

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“…See [11], [34], [35] for more applications of particle filters in degradation analysis. The particle filter is a sequential Monte Carlo method grounded in particle representations and can be viewed as the generalization of the well-known KF [36].…”

Section: B Improved Particle Filtering Algorithmmentioning

confidence: 99%

Remaining Useful Life Prediction Based on an Adaptive Inverse Gaussian Degradation Process With Measurement Errors

Chen

Xin-li

et al. 2020

IEEE Access

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Remaining useful life (RUL) prediction plays a crucial role in prognostics and health management (PHM). Recently, the adaptive model-based RUL prediction, which is proven effective and flexible, has gained considerable attention. Most research on adaptive degradation models focuses on the Wiener process. However, since the degradation process of some products is accumulated and irreversible, the inverse Gaussian (IG) process that can describe monotonic degradation paths is a natural choice for degradation modelling. This article proposes a nonlinear adaptive IG process along with the corresponding state space model considering measurement errors. Then, an improved particle filtering algorithm is presented to update the degradation parameter and estimate the underlying degradation state under the nonGaussian assumptions in the state space model. The RUL prediction depending on historical degradation data is derived based on the results of particle methods, which can avoid high-dimensional integration. In addition, the expectationmaximization (EM) algorithm combined with an improved particle smoother is developed to estimate and adaptively update the unknown model parameters once newly monitored degradation data become available. Finally, this article concludes with a simulation study and a case application to demonstrate the applicability and superiority of the proposed method.INDEX TERMS Adaptive model, inverse Gaussian process, measurement errors, remaining useful life.

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A Novel Degradation Modeling and Prognostic Framework for Closed-Loop Systems With Degrading Actuator

Cited by 38 publications

References 29 publications

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap

Remaining Useful Life Prediction Based on an Adaptive Inverse Gaussian Degradation Process With Measurement Errors

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