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Fault diagnosis of multistage manufacturing systems based on rough set approach

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Abstract

Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method.

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Authors and Affiliations

  1. Sino-German College of Applied Sciences, Tongji University, 4800 Caoan, Shanghai, 201804, China

    Nan Xie

  2. College of Computer and Information, Shanghai Second Polytechnic University, 2360, Jinhai Road, Shanghai, 201209, China

    Lin Chen

  3. Institute of Advanced Manufacturing Technology, Tongji University, 1239 Siping Road, Shanghai, 200092, China

    Aiping Li

Authors
  1. Nan Xie
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  2. Lin Chen
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  3. Aiping Li
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Correspondence to Nan Xie.

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Xie, N., Chen, L. & Li, A. Fault diagnosis of multistage manufacturing systems based on rough set approach. Int J Adv Manuf Technol 48, 1239–1247 (2010). https://doi.org/10.1007/s00170-009-2324-0

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  • DOI: https://doi.org/10.1007/s00170-009-2324-0

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