Robust Fault Diagnosis by Using Bond Graph Approach

Djeziri, Mohand; Merzouki, Rochdi; Bouamama, Belkacem Ould; Dauphin-Tanguy, G.

doi:10.1109/tmech.2007.912746

Cited by 89 publications

(54 citation statements)

References 21 publications

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“…To accommodate sensor noise and model imperfections, we employ the Z-test [32] to robustly determine if the residual is nonzero using a sliding window technique [33]. Other techniques for fault detection are also applicable [34], [35].…”

Section: Diagnosis Approachmentioning

confidence: 99%

A Qualitative Event-Based Approach to Continuous Systems Diagnosis

Daigle

Koutsoukos

Biswas

2009

IEEE Trans. Contr. Syst. Technol.

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Abstract-Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discrete-event diagnosis methods are used extensively, they do not easily address parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel event-based approach for diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. From a continuous model of the system, we systematically derive dynamic fault signatures expressed as event-based fault models, which are used, in turn, for designing an event-based diagnoser of the system and determining system diagnosability. The proposed approach is applied to a subset of the Advanced Diagnostics and Prognostics Testbed, which is representative of a spacecraft's electrical power system. We present experimental results from the actual testbed, as well as detailed simulation experiments that examine the performance of our diagnosis algorithms under different fault magnitudes and noise levels.Index Terms-Discrete-event system (DES), electrical power systems, model-based diagnosis.

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Section: Diagnosis Approachmentioning

confidence: 99%

A Qualitative Event-Based Approach to Continuous Systems Diagnosis

Daigle

Koutsoukos

Biswas

2009

IEEE Trans. Contr. Syst. Technol.

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“…They work well if there are few uncertainties in the system (see, for example, [1], [4], [5] and [6]). The second method, passive fault detection, develops robust decision making methods that utilize an adaptive threshold to achieve robustness [7], [8]. Performance measures that tradeoff accuracy versus false alarm rates are often used to derive the threshold values.…”

Section: Introductionmentioning

confidence: 99%

“…Djeziri, et al [7] present a method for handling parameter uncertainties in the model by deriving adaptive thresholds to avoid false alarms. The uncertainties in the parameters affect the prediction of system dynamics, and this is accounted for in the residuals.…”

Section: Introductionmentioning

confidence: 99%

Robust residual selection for fault detection

Khorasgani

Jung

Biswas

et al. 2014

53rd IEEE Conference on Decision and Control

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Abstract-A number of residual generation methods have been developed for robust model-based fault detection and isolation (FDI). There have also been a number of offline (i.e., design-time) methods that focus on optimizing FDI performance (e.g., trading off detection performance versus cost). However, design-time algorithms are not tuned to optimize performance for different operating regions of system behavior. To do this, would need to define online measures of sensitivity and robustness, and use them to select the best residual set online as system behavior transitions between operating regions. In this paper we develop a quantitative measure of residual performance, called the detectability ratio that applies to additive and multiplicative uncertainties when determining the best residual set in different operating regions. We discuss this methodology and demonstrate its effectiveness using a case study.

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“…In Djeziri et al [6,7,26], a robust diagnosis method with respect to parameter uncertainties has been developed using the bond graph approach. The method has been applied to a real dynamic system in order to detect the effect of the backlash phenomenon on the dynamics of a mechatronic system.…”

Section: Introductionmentioning

confidence: 99%