Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor design
Abstract A novel multiple Kalman filtre (KF)‐based scheme is proposed, as a generalisation of conventional gain‐scheduling techniques, for fault diagnosis and tolerance in a large class of multiple‐input and multiple‐output non‐linear systems. The outputs are corrupted by unknown stochastic disturba...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-03-01
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| Series: | IET Control Theory & Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cth2.12032 |
| _version_ | 1798033377372143616 |
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| author | Rajamani Doraiswami Lahouari Cheded |
| author_facet | Rajamani Doraiswami Lahouari Cheded |
| author_sort | Rajamani Doraiswami |
| collection | DOAJ |
| description | Abstract A novel multiple Kalman filtre (KF)‐based scheme is proposed, as a generalisation of conventional gain‐scheduling techniques, for fault diagnosis and tolerance in a large class of multiple‐input and multiple‐output non‐linear systems. The outputs are corrupted by unknown stochastic disturbance and measurement noise. A reliable and computationally efficient, two‐stage identification of a piecewise linear parameter‐varying Box–Jenkins dynamic model that better approximates the non‐linear system, at each operating point, and the design of the associated KFs are proposed. Novel emulators, whose induced parameter changes mimic likely and predictive operating scenarios, are used to provide an accurate model identification and robustness to noise, disturbance, non‐linearity errors and model perturbations. These crucial emulators generate missing representative data, aid predictive analytics, and improve the reliability and accuracy of the identified KF model. A novel formulation of the KF is used for fault isolation, and the Bayes strategy is used to isolate difficult‐to‐detect incipient faults in noisy environments. The proposed scheme leads to the design of a novel robust soft sensor aimed at replacing the maintenance‐prone hardware sensor in practical applications including product quality assessment, performance monitoring, condition‐based maintenance, fault diagnosis and fault‐tolerant control. The proposed soft sensor was successfully evaluated on simulated and laboratory‐scale physical control systems. |
| first_indexed | 2024-04-11T20:29:25Z |
| format | Article |
| id | doaj.art-85857d3b26ae4e36a6cfc34bc8b2c0ad |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-04-11T20:29:25Z |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-85857d3b26ae4e36a6cfc34bc8b2c0ad2022-12-22T04:04:33ZengWileyIET Control Theory & Applications1751-86441751-86522021-03-0115449951110.1049/cth2.12032Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor designRajamani Doraiswami0Lahouari Cheded1Department of Electrical and Computer Engineering University of New Brunswick Fredericton New Brunswick CanadaProfessor, Independent Researcher and Consultant Life Senior Member of IEEE UKAbstract A novel multiple Kalman filtre (KF)‐based scheme is proposed, as a generalisation of conventional gain‐scheduling techniques, for fault diagnosis and tolerance in a large class of multiple‐input and multiple‐output non‐linear systems. The outputs are corrupted by unknown stochastic disturbance and measurement noise. A reliable and computationally efficient, two‐stage identification of a piecewise linear parameter‐varying Box–Jenkins dynamic model that better approximates the non‐linear system, at each operating point, and the design of the associated KFs are proposed. Novel emulators, whose induced parameter changes mimic likely and predictive operating scenarios, are used to provide an accurate model identification and robustness to noise, disturbance, non‐linearity errors and model perturbations. These crucial emulators generate missing representative data, aid predictive analytics, and improve the reliability and accuracy of the identified KF model. A novel formulation of the KF is used for fault isolation, and the Bayes strategy is used to isolate difficult‐to‐detect incipient faults in noisy environments. The proposed scheme leads to the design of a novel robust soft sensor aimed at replacing the maintenance‐prone hardware sensor in practical applications including product quality assessment, performance monitoring, condition‐based maintenance, fault diagnosis and fault‐tolerant control. The proposed soft sensor was successfully evaluated on simulated and laboratory‐scale physical control systems.https://doi.org/10.1049/cth2.12032Other topics in statisticsOther topics in statisticsControl system analysis and synthesis methodsNonlinear control systemsAerospace controlInterpolation and function approximation (numerical analysis) |
| spellingShingle | Rajamani Doraiswami Lahouari Cheded Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor design IET Control Theory & Applications Other topics in statistics Other topics in statistics Control system analysis and synthesis methods Nonlinear control systems Aerospace control Interpolation and function approximation (numerical analysis) |
| title | Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor design |
| title_full | Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor design |
| title_fullStr | Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor design |
| title_full_unstemmed | Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor design |
| title_short | Fault tolerance in non‐linear systems: A model‐based approach with a robust soft sensor design |
| title_sort | fault tolerance in non linear systems a model based approach with a robust soft sensor design |
| topic | Other topics in statistics Other topics in statistics Control system analysis and synthesis methods Nonlinear control systems Aerospace control Interpolation and function approximation (numerical analysis) |
| url | https://doi.org/10.1049/cth2.12032 |
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