Application of the digraph method in system fault diagnostics

Kelly, Emma M.; Bartlett, L.M.

doi:10.1109/ares.2006.31

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Apart from the field of mechanical and industrial engineering, digraphs have found a common use to deal with problems in fault reduction and various other analyses. Computer analysis of general linear network using a digraph (McClenahan and Chan, 2004), system fault diagnostics (Kelly and Bartlett, 2006) for finding out the causes of failure and using a backward approach to reduce them, and hence making the system safer and various other such uses have been seen. Agrawal et al (2016) have applied graph theory and matrix approach for analyzing disposition decisions in reverse logistics.…”

Section: Research Methodology – Graph Theory Matrix Approachmentioning

confidence: 99%

Measuring the flexibility index for a supply chain using graph theory matrix approach

Singh

Kumar

2019

JGOSS

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Purpose In complex business environment of today, organizations are trying to be flexible and agile for meeting dynamic demands of customers in terms of product variety, cost cutting and decreasing product life cycle. The main purpose of this paper is to identify different attributes of flexibility and to propose an approach for evaluating flexibility index of a supply chain. Design/methodology/approach Total 33 factors are identified from the literature and further clustered into six major groups. They are top management commitment, organizational factors, mutual understanding, flow of information, relationship and decision-making and responsiveness. On the basis of these factors, an index has been developed by using graph theory matrix approach to evaluate the flexibility intensity of the supply chain for the firm in consideration. Findings The proposed approach has been illustrated in context to an Indian automobile organization, and value of the flexibility index has been evaluated. Framework proposed will help the supply chain managers to evaluate the flexibility index of their respective organizations. Research limitations/implications As values taken for different flexibility sub-factors are based on inputs given by managers of case organization, chances of bias may be there. Therefore, a study may be validated with more case studies to validate the proposed framework. Based on the flexibility index, organizations can find their strong and weak areas for strategy development. Originality/value A firm can also compare the flexibility index of its own supply chain with other’s supply chain or with the best in the industry for benchmarking purpose. Benchmarking of flexibility will help organizations in developing strategies to compete in dynamic market scenario.

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Section: Research Methodology – Graph Theory Matrix Approachmentioning

confidence: 99%

Measuring the flexibility index for a supply chain using graph theory matrix approach

Singh

Kumar

2019

JGOSS

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“…-Causal model -Signed directed graph (SDG) [Kelly 2006, Maurya 2007b -Fault tree [Hurdle 2009, Bartlett 2005 Model--Hierarchical model -Functional [Larsson 1994, Xiaojun 2009 based methods…”

Section: Data Based Methodsmentioning

confidence: 99%

“…tive models such as: signed directed graphs (SDG) [Chang 1999, Maurya 2007b, Kelly 2006, fault trees [Bartlett 2005, Hurdle 2009], or from hierarchical functional models [Larsson 1994, Xiaojun 2009].…”

Section: State Of the Artmentioning

confidence: 99%

“…This exchange represents the fundamental notions of physics, and the modeling reasoning consists on associating to each physical component of the system a basic element representing its physical phenomena. This methodology is domain-general, and it has been applied to a number of modeling applications that include electromechanical [ Van-Dijk 1994], thermofluidic and thermal [Thoma 2000], and chemical and thermochemical [Karnopp 1990] systems. Actually, four levels of modeling can be represented by a bond graph model [Samantaray 2008b].…”

Section: Recoverability Analysismentioning

confidence: 99%

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Bond Graph Model Based on Structural Diagnosability and Recoverability Analysis: Application to Intelligent Autonomous Vehicles

Loureiro

Merzouki

Bouamama

2012

IEEE Trans. Veh. Technol.

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It is time to write the acknowledgments. I will do my best. First of all, I would like to show my gratitude towards my supervisors, Mr. Belkacem Ould-Bouamama and Mr. Rochdi Merzouki for providing me with the opportunity to work on this interesting topic. Without whose support, availability, encouragement and fruitful discussions, I would not have achieved the results I obtained in my Ph.D. thesis. It was through his persistence, understanding and kindness that I always worked with enthusiasm. My gratitude also goes towards Mr. Taha Boukhobza and Mr. Eric Bideaux for their availability and interest on reviewing my work. I express to them my deepest appreciation. I also thank Ms. Geneviève Dauphin-Tanguy, Mr. François Charpillet and Mr. Mohamed Djemai for agreeing to be part of the jury of this thesis. Thank you to doctoral and post-doctoral researcher at the Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS). With them, I was able to share the joys and difficulties faced during a Ph.D. thesis. More specifically, I would like to extend very special thanks my office colleagues Samir Benmoussa and Youcef Touati for the helpful discussions and their useful suggestions during these three years.Thank you to all persons who were not mentioned above, but were involved with our work, directly and indirectly.Finally, thanks to my family for the support they provided me through my entire life. I must acknowledge Alice, without whose encouragement and assistance, I would not have worked serenely.

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“…The problem of safety monitoring and the subsequent control of off-nominal situations is in the safety literature tied to that of fault diagnosis and detection [Venkatasubramanian et al, 2003a]. The approaches to fault diagnosis can be divided into two big families: those that are aimed at detecting problems in off-line contexts, and those that do so on-line (or in real-time) [Kelly and Bartlett, 2006]. Among off-line approaches, it is possible to recognize sequential testing of system (during development stages) and the classical approaches of PRA (e.g., FMEA, FTA, and HAZOP techniques).…”

Section: Safety Monitoring and Fault Detection: Off-line And On-line mentioning

confidence: 99%

Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions

Favaró

Saleh

2016

Reliability Engineering & System Safety

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Probabilistic Risk Assessment (PRA) is a staple in the engineering risk community, and it has become to some extent synonymous with the entire quantitative risk assessment undertaking. Limitations of PRA continue to occupy researchers, and workarounds are often proposed. After a brief review of this literature, we propose to address some of PRA's limitations by developing a novel framework and analytical tools for model-based system safety, or safety supervisory control, to guide safety interventions and support a dynamic approach to risk assessment and accident prevention. Our work shifts the emphasis from the pervading probabilistic mindset in risk assessment toward the notions of danger indices and hazard temporal contingency. The framework and tools here developed are grounded in Control Theory and make use of the state-space formalism in modeling dynamical systems. We show that the use of state variables enables the definition of metrics for accident escalation, termed hazard levels or danger indices, which measure the "proximity" of the system state to adverse events, and we illustrate the development of such indices. Monitoring of the hazard levels provides diagnostic information to support both online and offline safety interventions. For example, we show how the application of the proposed tools to a rejected takeoff scenario provides new insight to support pilots' go/no-go decisions. Furthermore, we augment the traditional state-space equations with a hazard equation and use the latter to estimate the times at which critical thresholds for the hazard level are (b)reached. This estimation process provides important prognostic information and produces a proxy for a time-to-accident metric or advance notice for an impending adverse event. The ability to estimate these two hazard coordinates, danger index and time-to-accident, offers many possibilities for informing system control strategies and improving accident prevention and risk mitigation. Finally we develop a visualization tool, termed hazard temporal contingency map, which dynamically displays the "coordinates" of a portfolio of hazards. This tool is meant to support operators' situational awareness by providing prognostic information regarding the time windows available to intervene before hazardous situations become unrecoverable, and it helps decision-makers prioritize attention and defensive resources for accident prevention. In this view, emerging risks and hazards are dynamically prioritized based on the temporal vicinity of their associated accident(s) to being released, not on probabilities or combination of probabilities and consequences, as is traditionally done (offline) in PRA.This approach offers novel capabilities, complementary to PRA, for improving risk assessment and accident prevention. It is hoped that this work helps to expand the basis of risk assessment beyond its reliance on probabilistic tools, and that it serves to enrich the intellectual toolkit of risk researchers and safety professionals.

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Application of the digraph method in system fault diagnostics

Cited by 5 publications

References 12 publications

Measuring the flexibility index for a supply chain using graph theory matrix approach

Measuring the flexibility index for a supply chain using graph theory matrix approach

Bond Graph Model Based on Structural Diagnosability and Recoverability Analysis: Application to Intelligent Autonomous Vehicles

Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions

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