A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map

Wee, Yit Yin; Cheah, Wooi Ping; Tan, Shing Chiang; Wee, Kuokkwee

doi:10.1016/j.eswa.2014.06.037

Cited by 51 publications

(23 citation statements)

References 17 publications

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“…In [14], an RCA methodology is presented that focuses on the sustainability of biodiesel processes depicting how important such an analysis can be for the domain of chemical engineering. Furthermore, in [15], a combined method of RCA and inference mechanisms is presented. It is used for a start-up failure analysis in automobiles and offers better handling of the data versus conventional methods.…”

Section: State Of the Artmentioning

confidence: 99%

A Knowledge-Enriched Problem Solving Methodology for the Design Phase of Manufacturing Equipment

Mourtzis¹,

Doukas²,

Skrepetos³

2015

Procedia CIRP

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During the design phase of large-scale industrial engineering projects, numerous problems usually arise, which are related to the project analysis and methods used in each phase. Such projects are undertaken by multi-disciplinary teams of expert designers and planning managers that often communicate these problems to the members of their team via emails and telephone calls. However, the alignment of efforts towards solving these problems cannot be achieved effectively though these inherently limited communication and collaboration means. In parallel, the operation of manufacturing systems is evolving towards structures that are similar to social networks. The trend is to automatically connect to web-based collaboration platforms in order to search for the appropriate experts to deal with the problem in question. Towards this end, the proposed research work focuses on the development of a knowledge-based method, which is developed into a mobile app, and focuses on providing a collaboration space for reporting, tracking, and solving project-related problems. The methodology adopts concepts from the Root Cause Analysis method, which is further enriched with knowledge reuse features. Already reported and potentially solved problems, as well as other historical data are retrieved from a knowledge repository through advanced indexing techniques in order to support the identification of solutions for newly experienced problems. Results from the interpretation of the natural language that is considered to be used in the problem reporting phase and the accuracy of retrieved solutions are further discussed. Finally, the methodology is validated through a real-life case study obtained from the die construction department of an automotive industry.

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Section: State Of the Artmentioning

confidence: 99%

A Knowledge-Enriched Problem Solving Methodology for the Design Phase of Manufacturing Equipment

Mourtzis¹,

Doukas²,

Skrepetos³

2015

Procedia CIRP

View full text Add to dashboard Cite

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“…Lu et al (2014) suggested a novel modeling and prediction approach of time series based on synergy of high-order fuzzy cognitive map (HFCM) and fuzzy c-means clustering. Wee et al (2015) provide a powerful root cause analysis capability using Bayesian belief network and an intuitive presentation of causal knowledge using FCM.…”

Section: Cognitive Mapmentioning

confidence: 99%

Integration of ontology-based decision support and a cognitive map approach to predict flat-screen TV market trends

Lee

Bae

Lee

2015

Technological Forecasting and Social Change

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“…Bayesian networks may be used for root cause analysis in the uncertain environment as have been highlighted by several researchers. Bayesian networks link variables with probabilities to calculate posterior probabilities of outcome states supporting an efficient evidence propagation mechanism [36,37]. Bayesian inference has been quite successful for ecological research and environmental decision making because it has the potentiality to handle multi-criteria and multi-attribute decision problems [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of environmental bottleneck using Bayesian Networks: a case study of an Indian pig iron manufacturing organization

Sen

2015

Manufacturing Rev.

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-Environmentally conscious manufacturing has become a global attention for the iron and steel manufacturers to prevent global warming and climate change while making money. Iron and steel sector is considered as one of the most polluting sectors in the world. It is also one of the most energy intensive industries. During pig iron manufacturing, there is a number of steps that affect the environment emitting different pollutants. While some step(s) may be considered critical to damage the environment among all the steps, some pollutant(s) may be considered critical to affect the environment among all the pollutants. This paper proposes environmental bottleneck to consider critical step and critical pollutant simultaneously. Unless environmental bottleneck is improved, environmental performance of the entire manufacturing process may not improve significantly even if other processes (i.e. other than environmental bottleneck) are taken care of. Thus, environmental bottleneck must be taken care of properly by the manufacturing organization to enable environmentally conscious manufacturing. Hence, a method should be developed to identify environmental bottleneck. Current research work uses Bayesian Networks (BN) to identify environmental bottleneck. The contribution of the paper is to identify the environmental bottleneck for an Indian pig iron manufacturing organization. Results suggest that carbon monoxide (CO) emission from the blast furnace is the environmental bottleneck for the current pig iron manufacturing organization. Hence, proper precautions should be considered to control the CO emission from the blast furnace.

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A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map

Cited by 51 publications

References 17 publications

A Knowledge-Enriched Problem Solving Methodology for the Design Phase of Manufacturing Equipment

A Knowledge-Enriched Problem Solving Methodology for the Design Phase of Manufacturing Equipment

Integration of ontology-based decision support and a cognitive map approach to predict flat-screen TV market trends

Identification of environmental bottleneck using Bayesian Networks: a case study of an Indian pig iron manufacturing organization

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