Real time algorithm based on time series data abstraction and hybrid bond graph model for diagnosis of switched system

Taktak, Mariam; Triki, Slim; Kamoun, Anas

doi:10.1016/j.engappai.2016.12.009

Cited by 16 publications

(5 citation statements)

References 28 publications

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“…Hybrid Model Based Diagnosis There is a lot of work available that deals with the diagnosis of hybrid systems. Hybrid systems are mostly modeled as a combination of an underlying discrete model, where each state (we call it mode in this work) contains the representation of the continuous signals, such as (Biswas et al 2003;Grastien 2014;Taktak, Triki, and Kamoun 2017;Prakash, Samantaray, and Bhattacharyya 2017). We follow this paradigm in our work.…”

Section: Related Workmentioning

confidence: 99%

Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

Bunte

Stein

Niggemann

2019

AAAI

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This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

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Section: Related Workmentioning

confidence: 99%

Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

Bunte

Stein

Niggemann

2019

AAAI

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“…It was shown that the number of ARRs which can be obtained from a BG in preferred derivative causality is equal to the number of junctions having at least an associated detector, plus the number of redundant detectors. 15–19,38 However, this last statement is not always valid. The causality inversion method only finds part of the possible solution set, because this method does not exploit all possible sensor combinations.…”

Section: Bg Modeling and Robust Fdimentioning

confidence: 99%

“…The latter has been developed to generate the analytical redundancy relations ( ARRs ) in the presence of uncertainties. 17–19 This method is based on the generation of ARRs from the BG model in preferred derivative causality, where the number of ARRs is equal to the number of junctions having at least an associated detector, plus the number of redundant detectors. These ARRs can then be used to build the fault signature matrix (FSM) in order to isolate the faults.…”

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:

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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.

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“…Mosterman P J and Biswas G proposed the temporal causal graph (TCG) method [9], where TCG is a linear causal graph that connects variable nodes to represent the structure of the model. The method that introduces TCG is more systematic than the other ways for generating ARRs based on the bond graph because it has the advantages of avoiding causality and obtaining further ARRs [10].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of the Three-phase Asynchronous Motor Bond Graph Model Based on Bond Graph and Temporal Causal Graph

Feng

Wang

et al. 2023

J. Phys.: Conf. Ser.

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The bond graph and temporal causal graph (TCG) are proposed to diagnose the fault of three-phase asynchronous motors. The bond graph model of the three-phase asynchronous motor is constructed using the general equations in the three-phase coordinate system. The analytical redundancy relationships (ARRs) and fault signature matrix (FSM) of the three-phase asynchronous motor are derived systematically by introducing TCG to improve the system fault isolability. Simulation experiments are conducted for the rotor-turn short-circuit fault and the rotor-break bar fault of the three-phase asynchronous motor. The experimental results show that introducing TCG for fault diagnosis in this model is feasible.

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Real time algorithm based on time series data abstraction and hybrid bond graph model for diagnosis of switched system

Cited by 16 publications

References 28 publications

Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

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

Fault Diagnosis of the Three-phase Asynchronous Motor Bond Graph Model Based on Bond Graph and Temporal Causal Graph

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