An unknown input observer-based decentralized fault detection and isolation for a class of large-scale interconnected nonlinear systems

Abbasi, Ahmad; Poshtan, Javad

doi:10.1177/0142331217707570

Cited by 10 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address the aforementioned issue, many distributed fault detection and isolation approaches have been developed (Abbasi and Poshtan, 2018). Centralized and distributed attack detection and identification monitors were established for cyber-physical systems to identify unexpected faults and attacks (Pasqualetti et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Fast fault detection for nonlinear interconnected systems via deterministic learning

Zeng

Chen

Xing

et al. 2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, a fast fault detection scheme is developed for a class of nonlinear interconnected systems with output measurements. First, through combining an adaptive high gain observer with the deterministic learning theory, the system states and unknown dynamics are estimated simultaneously. However, large value of gain may let the estimator becomes noise sensitive. Thus, the observer structure is modified to avoid this issue. Second, by reusing the estimated knowledge which is stored in the constant radial basis function (RBF) neural networks, a bank of dynamic estimators are constructed. Then, the average [Formula: see text] norms of residuals are generated. The smallest residual principle is considered for decision-making in the detection phase. Third, the fault detection conditions and detection time are analyzed under the influence of observer gain. A simulation example is utilized to illustrate the effectiveness of this scheme.

show abstract

Section: Introductionmentioning

confidence: 99%

Fast fault detection for nonlinear interconnected systems via deterministic learning

Zeng

Chen

Xing

et al. 2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…More recently, due to the complex/harsh work environments, model uncertainties have to be taken into account in the course of system analysis/synthesis, shifting the focuses on robust model-based fault diagnosis fields. The connotation behinds these methods relevant to robust fault diagnosis applications, is either to construct a fault indicator to generate residual that is sensitive to specific faults and robust to the uninterested informations simultaneously, or to design a fault estimator that shows fault amplitude and type appropriately under various of unexpected external inputs or mismatched terms, see, for example, Zhou et al (2018); Abbasi and Poshtan (2018); Li et al (2018a); Zhao et al (2018); Wei et al (2017); and Shokouhi-Nejad et al (2019) for some recent research efforts.…”

Section: Introductionmentioning

confidence: 99%

Fault estimation of linear discrete time-varying systems with multiplicative noise based on finite impulse response filter

Liu

et al. 2019

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper aims to construct a finite impulse response (FIR) based fault estimator for a class of linear discrete time-varying systems (LDTV) with multiplicative noise. Drawing support of intensive stochastic analyses and matrix manipulations, a novel performance index is proposed such that the fault estimation error is minimized in stochastic sense. A necessary and sufficient condition is established to guarantee the existence of the FIR-based fault estimator with satisfied estimation accuracy. The optimal gain of the desired fault estimator is calculated in an analytical way by minimizing the aforementioned performance index. Several examples are presented to demonstrate the effectiveness and superiority of the proposed methods.

show abstract

“…In the observer approach, a bank of observer-based residuals is usually required, in order to realize fault isolation. The unknown input observer 8,9 is another FDI method by decoupling input disturbance and other faults in the corresponding residuals. Most of the developed approaches are only suited for the detection and isolation of sensor and actuator faults; they present a weakness in the isolation performance.…”

Section: Introductionmentioning

confidence: 99%

Uncertain fault estimation using bicausal bond graph: Application to intelligent autonomous vehicle

Lounici

Touati

Adjerid

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

This article addresses the fault detection and isolation problem of uncertain systems using the bond graph model–based approach. The latter provides through its causal and structural properties an automatic analytical redundancy relations generation. The numerical evaluation of analytical redundancy relations yields residuals, which are used to verify the coherence between the real system and reference behaviors describing the nominal operation. In fact, the residual is compared to its thresholds to detect the fault. In addition, the comparison between all fault signatures allows making a decision on fault isolation. Moreover, to isolate the faults that activate the same set of residuals, an additional residual must be calculated for each fault. This additional residual is the comparison between two estimations of the considered fault obtained using the sensitivity relations. However, due to the presence of uncertainties, errors can occur in the fault estimation allowing false decisions on fault isolation. The novelties and innovative interests in the present work are (1) to improve the fault estimation procedure based on the uncertainties modeling and bicausality notion, in order to overcome the problem related to errors in the estimated fault and (2) to suitably generate the isolation thresholds in a systematic way using the uncertain fault estimation procedure proposed in this article so that fault can be isolated successfully. The proposed methodology is studied under various scenarios via simulations over an electromechanical traction system corresponding to a quarter of intelligent autonomous vehicle, named RobuCar.

show abstract

An unknown input observer-based decentralized fault detection and isolation for a class of large-scale interconnected nonlinear systems

Cited by 10 publications

References 28 publications

Fast fault detection for nonlinear interconnected systems via deterministic learning

Fast fault detection for nonlinear interconnected systems via deterministic learning

Fault estimation of linear discrete time-varying systems with multiplicative noise based on finite impulse response filter

Uncertain fault estimation using bicausal bond graph: Application to intelligent autonomous vehicle

Contact Info

Product

Resources

About