An Improved Zonotopic Approach Applied to Fault Detection for Takagi–Sugeno Fuzzy Systems

Ma, Youdao; Wang, Zhenhua; Meslem, Nacim; Raïssi, Tarek; Shen, Yi

doi:10.1109/tfuzz.2023.3267076

Cited by 15 publications

(4 citation statements)

References 34 publications

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“…Proof. According to (19), it is assumed that the two sets R ζ,i FI,k and R η,i FI,k are separated at k = k i under u i FI = u i vx . Hence, by solving problem (13) again under the above conditions, the minimum distance points of the two sets (denoted by (ζ m * k , η m * k ) and different from (ζ m k , η m k ) presented in Theorem 1) can be obtained.…”

Section: }mentioning

confidence: 99%

“…Depending on the description of the uncertainty in the system, the reported methods can be classified as deterministic [ 16 ], stochastic [ 17 ], and hybrid methods [ 18 ]. This present research focuses on the AFD of dynamic systems with a deterministic uncertainty, i.e., an uncertainty with known bounded sets [ 19 , 20 ]. The relevant methods in the literature are commonly called set-based or set-theoretic AFD methods and the objective is to design an auxiliary input signal to separate healthy and faulty sets in a finite time.…”

Section: Introductionmentioning

confidence: 99%

“…and go to step 3; Determine the values of , k i that minimize the evaluation function + k/ ki in(19); 15: Choose different vertices u i vx of U , and repeat steps 2-14 to find , k i that can further decrease + k/ ki ; 16: Output the final isolation decision delay k i , auxiliary excitation u i vx , and associated separating function Iso…”

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confidence: 99%

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Active Fault Isolation for Multimode Fault Systems Based on a Set Separation Indicator

Han

Liu

et al. 2023

Entropy

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This paper considers the active fault isolation problem for a class of uncertain multimode fault systems with a high-dimensional state-space model. It has been observed that the existing approaches in the literature based on a steady-state active fault isolation method are often accompanied by a large delay in making the correct isolation decision. To reduce such fault isolation latency significantly, this paper proposes a fast online active fault isolation method based on the construction of residual transient-state reachable set and transient-state separating hyperplane. The novelty and benefit of this strategy lies in the embedding of a new component called the set separation indicator, which is designed offline to distinguish the residual transient-state reachable sets of different system configurations at any given moment. Based on the results delivered by the set separation indicator, one can determine the specific moments at which the deterministic isolation is to be implemented during online diagnostics. Meanwhile, some alternative constant inputs can also be evaluated for isolation effects to determine better auxiliary excitation signals with smaller amplitudes and more differentiated separating hyperplanes. The validity of these results is verified by both a numerical comparison and an FPGA-in-loop experiment.

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Section: }mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active Fault Isolation for Multimode Fault Systems Based on a Set Separation Indicator

Han

Liu

et al. 2023

Entropy

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“…State estimation plays a crucial role in many fields, such as controller design [1][2][3][4][5] and model-based fault diagnosis. [6][7][8] In recent years, interval estimation has received widespread attention and many interval estimation methods have been proposed for fault detection [9][10][11][12] and model predictive control. [13][14][15] Existing methods about interval estimation can generally be divided into two categories: interval observer and set-based interval estimation.…”

Section: Introductionmentioning

confidence: 99%

Less‐conservative reduced‐order interval estimation: A new two‐step approach

Wang,

Zhang,

Meslem

et al. 2024

Intl J Robust & Nonlinear

Self Cite

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This article proposes a two‐step interval estimation method for continuous‐time linear time‐invariant systems with unknown but bounded disturbances and measurement noise. In the first step, method is used to design a robust reduced‐order observer that attenuates the impact of the system uncertainties on the estimation error. In the second step, a non‐conservative interval prediction of the estimation error is achieved by applying a symmetric set‐integration approach. The resulting tight set‐valued state estimation is obtained by combining the point estimation from the reduced‐order observer with the predicted estimation error set provided by the set‐integration method. Compared to the conventional methods, the proposed method is built on less‐restrictive design conditions, that allow one to deal with a large range of applications. On the other hand, numerical simulations show that the proposed method offers more precise interval estimation compared to an optimal reduced‐order interval observer selected from recent literature.

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Granular Fuzzy Model with High Order Singular Values Decomposition and Hesitation Fuzzy Granularity

Zhao,

Kang,

Hao

et al. 2024

Studies in Big Data

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An Improved Zonotopic Approach Applied to Fault Detection for Takagi–Sugeno Fuzzy Systems

Cited by 15 publications

References 34 publications

Active Fault Isolation for Multimode Fault Systems Based on a Set Separation Indicator

Active Fault Isolation for Multimode Fault Systems Based on a Set Separation Indicator

Less‐conservative reduced‐order interval estimation: A new two‐step approach

Granular Fuzzy Model with High Order Singular Values Decomposition and Hesitation Fuzzy Granularity

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