Template languages for fault monitoring of timed discrete event processes

Pandalai, D.N.; Holloway, L.E.

doi:10.1109/9.855548

Cited by 114 publications

(63 citation statements)

References 12 publications

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“…The general principle of our approach is the transposition in Petri Nets of the classical principle used in fault detection of continuous-variable models (Parity Space [9], Observers [15], Identification [26]). Remember that a large class of fault detection approaches relies on the different types of continuous-variable models while another class considers Discrete-Event Systems such as Petri Nets [17] [1] [12] [16] and Automata [22] [14] [23]. In this paper, changes (or faults) are considered as variations of dynamic models compared to a Petri Net which only describes the normal behavior.…”

Section: Introductionmentioning

confidence: 99%

State estimation and detection of changes in time interval models

Declerck

Guezzi

2012

Discrete Event Dyn Syst

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This paper presents an estimation approach for Time Event Graphs such as P-Time Event Graphs and Time Stream Event Graphs. It is assumed that the nominal behavior is known and that transitions are partitioned as observable and unobservable transitions. The technique is applied to the detection of changes which are (possibly small) finite variations of dynamic models compared to this nominal behavior. The detected changes provide indications that can be used in future maintenance operations.Using the algebra of dioids, the approach uses a receding-horizon estimation of the greatest state and analyzes the consistency of the data.

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Section: Introductionmentioning

confidence: 99%

State estimation and detection of changes in time interval models

Declerck

Guezzi

2012

Discrete Event Dyn Syst

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“…Practical experience has shown that detection and isolation of many classes of faults in dynamic systems can be approached as a problem of state estimation and inferencing for discrete event systems (Aghasaryan et al, 1998;Benveniste, 2003;Bouloutas, 1990;Console, 2000;Debouk et al, 2000;Garcia et al, 2002;Lafortune et al, 2001;Lamperti and Zanella, 1999;Lin, 1994;Lin et al, 1993;Lunze, 2000;Pandalai and Holloway, 2000;Pencole Â, 2000;Pencole Â et al, 2001;Sampath, 2001;Sampath et al, 1998Sampath et al, , 1995Sampath et al, , 1996Sengupta, 2001;Sinnamohideen, 2001;Westerman et al, 1998;Hastrudi Zad et al, 1998). In many systems, faulty behavior often occurs intermittently, with fault events followed by corresponding``reset'' events for these faults, followed by new occurrences of fault events, and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…Intermittent faults occur in software systems as well; consider for instance exceptions and interrupts that are caused by some unknown``bugs'' and that lead to crashes and reboots. The methodologies used in Aghasaryan et al (1998), Benveniste et al (2003), Bouloutas (1990), Console (2000), Debouk et al (2000), , , Lafortune et al (2001), Lamperti and Zanella (1999), Lin (1994), Lin et al (1993), Lunze (2000), Pandalai and Holloway (2000), Pencole Â (2000), Pencole Â et al (2001), Sampath (2001), Sampath et al (1998Sampath et al ( , 1995Sampath et al ( , 1996, Sengupta (2001), Sinnamohideen (2001), Westerman et al (1998) andHastrudi Zad et al (1998) assume that once faults occur, they remain in effect permanently; hence, the terminology``failures'' is often used for these permanent faults. Furthermore, to the best of our knowledge, diagnostic methodologies developed in the ®eld of model-based reasoning in arti®cial intelligence (which are close in spirit to the discrete event systems methodologies, since they are also based on qualitative system models) are also geared towards the diagnosis of permanent faults; see, for example, Darwiche and Provan (1996), Dvorak and Kuipers (1992), Chen (1998, 1999), and Williams and Nayak (1996).…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Intermittent Faults

Contant

Lafortune

Teneketzis

2004

Discrete Event Dynamic Systems

105

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Abstract. The diagnosis of``intermittent'' faults in dynamic systems modeled as discrete event systems is considered. In many systems, faulty behavior often occurs intermittently, with fault events followed by corresponding``reset'' events for these faults, followed by new occurrences of fault events, and so forth. Since these events are usually unobservable, it is necessary to develop diagnostic methodologies for intermittent faults. Prior methodologies for detection and isolation of permanent faults are no longer adequate in the context of intermittent faults, since they do not account explicitly for the dynamic behavior of these faults. This paper addresses this issue by: (i) proposing a modeling methodology for discrete event systems with intermittent faults; (ii) introducing new notions of diagnosability associated with fault and reset events; and (iii) developing necessary and suf®cient conditions, in terms of the system model and the set of observable events, for these notions of diagnosability. The de®nitions of diagnosability are complementary and capture desired objectives regarding the detection and identi®cation of faults, resets, and the current system status (namely, is the fault present or absent). The associated necessary and suf®cient conditions are based upon the technique of`d iagnosers'' introduced in earlier work, albeit the structure of the diagnosers needs to be enhanced to capture the dynamic nature of faults in the system model. The diagnosability conditions are veri®able in polynomial time in the number of states of the diagnosers.

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“…Only a few of them incorporate time information. In [1] condition templates are used to represent the system behavior with regard to event sequences and observation times. Timed fault detection is performed by observing whether an event occurred in an expected time interval or not.…”

Section: Introductionmentioning

confidence: 99%

Determination of timed transitions in identified discrete-event models for fault detection

Schneider

Litz

Lesage

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Abstract-Model-based fault detection compares modeled and observed behavior to decide whether a system operates properly or not. The key issue in this paper is to model large-scale Discrete Event Systems (DESs) with little a-priori knowledge. For this class of systems a new approach to blackbox determination of timed transitions for timed automata is proposed. The method identifies a set of time guards leading to an advantageous trade-off between the fault detection errors: false alarms and missed detections. A real industrial system is applied for evaluation of time guard determination. It is shown that applying the proposed method results in a better trade-off between the fault detection errors than using common methods (e.g. Min/Max, Normal Distribution).

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Template languages for fault monitoring of timed discrete event processes

Cited by 114 publications

References 12 publications

State estimation and detection of changes in time interval models

State estimation and detection of changes in time interval models

Diagnosis of Intermittent Faults

Determination of timed transitions in identified discrete-event models for fault detection

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