A discrete event systems approach to discriminating intermittent from permanent faults

Deng, Guanqian; Qiu, Jing; Liu, Guanjun; Li, Kehong

doi:10.1016/j.cja.2013.07.001

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…A more general framework is proposed in Deng et al (2014a), Deng et al (2014b), and Deng et al (2013), where fault models that include both permanent and intermittent faults are considered. In order to diagnose faults in such a setting, the authors propose an approach that first discriminates between the fault classes.…”

Section: Discriminating Intermittent Faults From Permanent Faultsmentioning

confidence: 99%

“…From the diagnosis point of view, it is important to distinguish between these fault types, especially between permanent and intermittent faults (Deng et al, 2014a). Intermittent faults can be spontaneously recovered by the occurrence of uncontrollable and unobservable reset events; therefore, the system oscillates between normal and faulty behavior.…”

mentioning

confidence: 99%

“…In the context of DES, intermittent faults are defined as "faults which often occur intermittently, and can be seen as fault events followed, later on, by the corresponding reset events, (possibly) followed by new occurrences of fault events, and so forth" (Contant et al, 2004). Another similar definition is given in Deng et al (2014a), where intermittent faults are presented as faults that can at some point automatically reset once they have occurred.…”

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confidence: 99%

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Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

Boussif

Ghazel²,

Basilio

2020

Discrete Event Dyn Syst

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Real life experience has shown that intermittent faults are among the most challenging kinds of faults to detect and isolate, being present in the majority of production systems. Such a concern has made intermittent fault an active area of research in both discrete event and continuous-variable dynamic systems. In this paper, we present a review of the state-of-the art of intermittent fault diagnosis of discrete event systems modeled by finite state automata. To this end, we revisit the main definitions of diagnosability of intermittent faults, and present comparisons between them, consider verification and analysis techniques, and discuss available complexity results. Examples are used throughout the paper to illustrate the reviewed concepts and verification algorithms. We also look ahead, by suggesting some perspectives for future research.

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Section: Discriminating Intermittent Faults From Permanent Faultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

Boussif

Ghazel²,

Basilio

2020

Discrete Event Dyn Syst

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“…Monekosso and Remagnino [13] described a data-driven method to detect and mask PFs and TFs using principal component analysis and canonical correlation analysis techniques. Deng et al [14] proposed a discrete-event system method to distinguish PFs and IFs. Liu et al [15] proposed an experimental method for detecting IFs in a field-programmable gate array of ball grid array packages, by injecting current into an input/output (I/O) port and detecting the voltage signal.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Cai

Liu

Xie

2017

IEEE Trans. Automat. Sci. Eng.

225

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Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology. Note to Practitioners-This paper is motivated by the problem of fault diagnosis of complex electronic systems in the presence of transient fault (TF) and intermittent fault (IF). Existing approaches do not involve the dynamic behavior of electronicsystems, i.e., degradation and aging. The results of fault diagnosis could be different at different times for the given identical fault symptoms because of the degradation and aging. This paper suggests a new fault diagnosis approach using dynamic Bayesian networks (DBNs). It aims at identifying the component faults and distinguishing the fault types, i.e., TF, IF, and permanent faults. We construct the DBN structure and parameter models and define two judgment rules to determine the fault diagnosis results. The future scope of work can be directed toward the development of a practice fault diagnosis system for an electronic system to validate the proposed methodology. Index Terms-Dynamic Bayesian network (DBN), fault diagnosis, intermittent fault (IF), transient fault (TF).

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“…Currently, the life prediction model mainly includes a physical model and a data driven model. Considering the randomness of intermittent failures, the data driven model is more suitable for the residual life prediction of electrical connectors [14][15][16][17][18][19]. Combined with the degradation processes of electrical connectors, it is helpful to improve the prediction accuracy of the residual life of electrical connectors by selecting a suitable model.…”

Section: Introductionmentioning

confidence: 99%

Research on Residual Life Prediction for Electrical Connectors Based on Intermittent Failure and Hidden Semi-Markov Model

Qiu

et al. 2018

Applied Sciences

Self Cite

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Based on the dynamic properties of electrical connector intermittent failure, the model and methods for residual life prediction for electrical connectors are studied in this paper. Firstly, the mechanism of electrical connector intermittent failure is analyzed, and the area enclosed by the contact resistance curve and the fault threshold is defined as the generalized severity of intermittent failure to describe how severe the electrical connector’s intermittent failure is. Then, the Hidden Semi-Markov Model (HSMM) is introduced to build the residual life prediction model of the electrical connector. Further, the evaluation method of using the state and prediction method for residual life are studied. Finally, by carrying out the residual life prediction test, the effectiveness of the residual life prediction method for electrical connectors based on intermittent failure and HSMM is verified.

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A discrete event systems approach to discriminating intermittent from permanent faults

Cited by 8 publications

References 20 publications

Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Research on Residual Life Prediction for Electrical Connectors Based on Intermittent Failure and Hidden Semi-Markov Model

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