Approximate Model-Based Diagnosis Using Greedy Stochastic Search

Feldman, Alexander; Provan, Gregory; Gemund, Arjan van

doi:10.1007/978-3-540-73580-9_13

Cited by 22 publications

(45 citation statements)

References 3 publications

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“…To increase the chance of finding a better solution without significantly increasing the computational complexity, a greedy stochastic search algorithm is proposed. Greedy stochastic search algorithms have been successfully applied in similar problems when computing minimal diagnosis candidates (Feldman, Provan, & van Gemund, 2010). Because of the similarities between the two problems, greedy stochastic search is considered a suitable candidate search algorithm for the sensor selection problem.…”

Section: Greedy Stochastic Searchmentioning

confidence: 99%

“…Because of the similarities between the two problems, greedy stochastic search is considered a suitable candidate search algorithm for the sensor selection problem. A general description of the algorithm in Feldman et al (2010) is given here.…”

Section: Greedy Stochastic Searchmentioning

confidence: 99%

“…If c * is the cost of optimal solution, it can be shown that the discrete distribution P(X min = c * ) → 1 when N → ∞ (see Theorem 2 in Feldman et al, 2010). The probability that y l ∈S min c l = c * can be written as…”

Section: Greedy Stochastic Searchmentioning

confidence: 99%

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Sensor selection for fault diagnosis in uncertain systems

Jung

Dong

Frisk

et al. 2018

International Journal of Control

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Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time. ARTICLE HISTORY

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Section: Greedy Stochastic Searchmentioning

confidence: 99%

Section: Greedy Stochastic Searchmentioning

confidence: 99%

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Sensor selection for fault diagnosis in uncertain systems

Jung

Dong

Frisk

et al. 2018

International Journal of Control

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“…One such alternative are techniques that are model-based. Although outside the realm of service-based computing, Feldman et al have proposed a greedy stochastic algorithm for computing diagnoses within a model-based diagnosis framework (Feldman et al 2010). An important drawback of these model-based approaches is that we need to provide a correct model of the nominal behavior of the entire service-based application, which is daunting.…”

Section: Fault Localizationmentioning

confidence: 99%

Analysis of service diagnosis improvement through increased monitoring granularity

2015

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Due to their loosely coupled and highly dynamic nature, service-oriented systems offer many benefits for realizing fault tolerance and supporting trustworthy computing. They enable automatic system reconfiguration when a faulty service is detected. Spectrum-based fault localization (SFL) is a statistics-based diagnosis technique that can be effectively applied to pinpoint problematic services. However, SFL exhibits poor performance in diagnosing services which are tightly interacted. Previous research suggests that an increase in the number of monitoring locations may improve the diagnosability for tight interaction. In this paper, we analyze the trade-offs between the diagnosis improvement through increased monitoring granularity and the overhead caused by the introduction of more monitors, when diagnosing tightly interacted faulty services. We apply SFL in a service-based system, for which we show that 100 % correct identification of faulty services can be achieved through the increased monitoring granularity. We assess the overhead with increased monitoring granularity and compare this with the original monitoring setup. Our experimental results show that the monitoring at the service communication level causes relatively high overhead, whereas the monitoring overhead at a finer level of granularity, i.e., at the service implementation level, is much lower, but highly dependent on the number of monitors deployed.& Cuiting Chen

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“…We have implemented a greedy stochastic algorithm called Safari (StochAstic Fault diagnosis AlgoRIthm, [8]). For weak fault models, it can compute 80-90% of all cardinality-minimal diagnoses, several orders of magnitude faster than state-of-the-art deterministic algorithms, such as CDAS, allowing systems with several hundreds of components to be diagnosed in seconds.…”

Section: Diagnostic Performancementioning

confidence: 99%

Automated Fault Diagnosis in Embedded Systems

Zoeteweij

Pietersma

Abreu

et al. 2008

2008 Second International Conference on Secure System Integration and Reliability Improvement

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Abstract. Automated fault diagnosis is emerging as an important factor in achieving an acceptable and competitive cost/dependability ratio for embedded systems. In this paper, we introduce model-based diagnosis and spectrum-based fault localization, two state-of-the-art approaches to fault diagnosis that jointly cover the combination of hardware and control software typically found in embedded systems. In this paper we present an introduction to the field, discuss our recent research results, and report on the application on industrial test cases.

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Approximate Model-Based Diagnosis Using Greedy Stochastic Search

Cited by 22 publications

References 3 publications

Sensor selection for fault diagnosis in uncertain systems

Sensor selection for fault diagnosis in uncertain systems

Analysis of service diagnosis improvement through increased monitoring granularity

Automated Fault Diagnosis in Embedded Systems

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