Discrete event system approach for delay fault analysis in digital circuits

Westerman, G.; Kumar, Ratnesh; Stroud, Charles E.; Heath, J.R.

doi:10.1109/acc.1998.694667

Cited by 21 publications

(9 citation statements)

References 15 publications

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“…D ETECTING system failures is an important and challenging problem in many disciplines such as software engineering [1], automotive systems [2], power systems [3], nuclear engineering [4], aerospace engineering [5], and digital circuits [6]. In general, a fault is a deviation of a system from its required or normal behavior, such as executing a fault-event, reaching a fault-state, or violating a system specification, and needs to be detected accurately within a tolerable delay bound to ensure timely activation of any fault tolerance actions.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Discrete-Time Stochastic Systems Subject to Temporal Logic Correctness Requirements

Chen

Kumar

2015

IEEE Trans. Automat. Sci. Eng.

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This paper studies the fault detection of discrete-time stochastic systems with linear-time temporal logic (LTL) as correctness requirement-A fault is a violation of LTL specification. The temporal logic allows system correctness properties to be specified compactly and in a user-friendly manner (being close to natural-languages), and supports automatic translation into other formal models such as automata. We introduce the notion of input-output stochastic hybrid automaton (I/O-SHA) and show that the refinement of a continuous physical system (modeled as stochastic difference equations) against a certain class of LTL correctness requirement can be modeled as an I/O-SHA. The refinement preserves the behaviors of the physical system and also captures requirement-violation as a reachability property. Probability distribution over the discrete locations of hybrid system is estimated recursively by computing the distributions for continuous variables for each discrete location. This is then used to compute the likelihood of fault, a statistic that we employ for the purpose of fault detection. The performance of the detection scheme is measured in terms of false alarm (FA) and missed detection (MD) rates, and the condition for the existence of a detector to achieve any desired rates of FA and MD is captured in form of Stochastic-Diagnosability, a notion that we introduce in this paper for stochastic hybrid systems. The proposed method of fault detection is illustrated by a practical example.Note to Practitioners-Many cyberphysical systems, such as building automation systems, automotive vehicles and smart power grids, can be modeled as stochastic systems with mixed continuous and discrete dynamics subject to disturbance and noise, whose behaviors are monitored and controlled by networked (digital) control systems. This paper investigates fault detection in the form of temporal logic specification violation in model-based approach, by transforming it into a state estimation problem for stochastic system models. We provide an algorithm for online fault detection and introduce the notion of Stochastic-Diagnosability for the existence of a detector with any desired accuracy of detection as measured by false alarms and missed detections. The work is illustrated by a room heating system example.

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Section: Introductionmentioning

confidence: 99%

Fault Detection of Discrete-Time Stochastic Systems Subject to Temporal Logic Correctness Requirements

Chen

Kumar

2015

IEEE Trans. Automat. Sci. Eng.

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“…A live, prefix-closed language is -AA-diagnosable with respect to a projection and a transition probability function if (23) where the diagnosability condition function is if otherwise (24) The system in Fig. 4 is -AA-diagnosable, because as becomes large, the probability that the system behavior that does not contain a failure approaches zero.…”

Section: Definition 3: (Aa-diagnosability)mentioning

confidence: 99%

Diagnosability of stochastic discrete-event systems

Thorsley

Teneketzis

2005

IEEE Trans. Automat. Contr.

198

228

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Abstract-We investigate diagnosability of stochastic discrete-event systems. We define the notions of A-and AA-diagnosability for stochastic automata; these notions are weaker than the corresponding notion of diagnosability for logical automata introduced by Sampath et al. Through the construction of a stochastic diagnoser, we determine offline conditions necessary and sufficient to guarantee A-diagnosability and sufficient to guarantee AA-diagnosability. We also show how the stochastic diagnoser can be used for on-line diagnosis of failure events. We illustrate the results through two examples from HVAC systems.Index Terms-Discrete-event systems, failure detection, fault diagnosis, probabilistic models, stochastic automata.

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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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Discrete event system approach for delay fault analysis in digital circuits

Cited by 21 publications

References 15 publications

Fault Detection of Discrete-Time Stochastic Systems Subject to Temporal Logic Correctness Requirements

Fault Detection of Discrete-Time Stochastic Systems Subject to Temporal Logic Correctness Requirements

Diagnosability of stochastic discrete-event systems

Diagnosis of Intermittent Faults

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