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Machine Failure Diagnosis Model Applied with a Fuzzy Inference Approach

  • Conference paper
Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 10))

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Abstract

This study presented the method of fuzzy failure diagnosis to support the development failure diagnosis system. The fuzzy evaluation is used to process the problems of which the failures and the symptoms are dealing with the uncertainty. In this study, we development machine failure diagnosis model by using both statistics and fuzzy compositional rule of inference methods. We use the statistical confidence interval instead of the point estimate and fuzzify confidence interval to triangular fuzzy numbers. In this study, we apply the centroid method to solve the estimated failure rate in the fuzzy sense to obtain the machine failure degree.

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Author information

Authors and Affiliations

  1. Department of International Business, China University of Technology, 56, Sec. 3, Hsing-Lung Road, Taipei, 116, Taiwan

    Lily Lin

  2. Department of Information Management, Chinese Culture University, 55, Hwa-Kung Road, Yang-Ming-San, Taipei, 11114, Taiwan

    Huey-Ming Lee

Authors
  1. Lily Lin
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  2. Huey-Ming Lee
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Editor information

Editors and Affiliations

  1. Graduate School of Information, Production and Systems (IPS), Waseda University, 2-7 Hibikino Wakamatsuku, 808-0135, Fukuoka, Kitakyushu, Japan

    Junzo Watada

  2. Sellinger School of Business and Management, Loyola University Maryland, 4501 N Charles Street, 21210, Baltimore, MD, USA

    Gloria Phillips-Wren

  3. School of Electrical and Information Engineering, University of South Australia, Mawson Lakes Campus Adelaide, 5095, South Australia, SA, Australia

    Lakhmi C. Jain

  4. KES International, Shoreham-by-seaWest Sussex, PO Box 2115, BN43 9AF, United Kingdom

    Robert J. Howlett

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Lin, L., Lee, HM. (2011). Machine Failure Diagnosis Model Applied with a Fuzzy Inference Approach. In: Watada, J., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22194-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-22194-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22193-4

  • Online ISBN: 978-3-642-22194-1

  • eBook Packages: EngineeringEngineering (R0)

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