A zonotope-based fault detection for multirate systems with improved dynamical scheduling protocols

Ju, Yamei; Liu, Hongjian; Ding, Derui; Sun, Yonghe

doi:10.1016/j.neucom.2022.06.003

Cited by 4 publications

(5 citation statements)

References 41 publications

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“…Finally, make additional temporal shift in (18), substitute in the obtained expression x + * 3 for the right hand side of its equation in (16) and substitute the argument of 𝜂 3 for ( 17) and (19):…”

Section: Nonlinear Parity Relations Designmentioning

confidence: 99%

“…It follows from the model ( 16) and subsequent transformations that if the addend of the form D * i d(t) enters into the first or second equation, then it will enter in (17) or (19) as an argument of the function 𝜂 2 or 𝜂 3 . This means that the interval parity relations can be designed but to proof Theorem 1, these functions should satisfy some conditions, in particular, they must be monotone for all arguments 35 or meet the special componentwise inequalities.…”

Section: Nonlinear Parity Relations Designmentioning

confidence: 99%

“…15,16 Exhaustive reviews are in References 12,17,18. For linear systems, there exits effective method to estimate the state vector with guaranteeing performances, so-called zonotopic observers. 19,20 However this method is inapplicable for nonlinear systems and systems with uncertainties while interval observers can be used in this case. 12 In References 16,21-26 the interval observers are use to solve the problem of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…For linear systems, there exits effective method to estimate the state vector with guaranteeing performances, so‐called zonotopic observers 19,20 . However this method is inapplicable for nonlinear systems and systems with uncertainties while interval observers can be used in this case 12 …”

Section: Introductionmentioning

confidence: 99%

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Interval parity relations design for fault diagnosis

Zhirabok,

Zuev,

Filaretov

2024

Adaptive Control & Signal

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SummaryThe problem of interval parity relations design for systems described by linear and nonlinear models under the external disturbances is considered. The problem is solved based on the reduced‐order model of the original system. The relations allowing designing interval parity relations insensitive or having minimal sensitivity to the disturbances are obtained. The obtained interval parity relations are used to solve the problem of fault diagnosis. Theoretical results are illustrated by example.

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Section: Nonlinear Parity Relations Designmentioning

confidence: 99%

Section: Nonlinear Parity Relations Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interval parity relations design for fault diagnosis

Zhirabok,

Zuev,

Filaretov

2024

Adaptive Control & Signal

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“…This approach aims to generate discerning signals that accurately capture the inconsistencies between normal and faulty system operations, showcasing its instrumental role in effectively addressing the challenges associated with FDI. Exemplifying the real-world utility of the approaches, noteworthy implication can be found in esteemed works such as [ 10 , 11 ]. Ten et al conducted research on innovative data-driven methods [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system

Mustafa,

Ahmed,

Basit

et al. 2024

PLoS ONE

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The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

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