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Research on Equipment Failure Risk Control System Based on the Knowledge Integration

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Information Computing and Applications (ICICA 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 308))

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Abstract

Due to the existing of complex precise tendency in mechanical equipment structure and the more damage brought by these mechanical equipment failure; It is necessary that the company should build an effective risk control system. The paper explains that setting up an equipment failure risk control system based on the knowledge integration; analyzes the object of knowledge integration in the equipment failure risk control by examples; meanwhile constructs a comprehensive assessment mold and realizes the equipment failure risk assessment under the NET platform.

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References

  1. Liu, J., Ji, H.P., Zhu, Q.X.: One Method to Determine the Optimal Maintenance Time Based on Selective Attrition. Advanced Materials Research, 139–141 (2010)

    Google Scholar 

  2. Liu, J., Ji, H.P., Zhu, Q.X.: Improved Multiple Minimum Supports Association Rules and Its Applications on Fault Diagnosis. In: Proc. of 2010 International Conference on Mechanical, Industrial and Manufacturing Technologies 2010, pp. 1669–1672. Advanced Materials Research, Guangzhou (2010)

    Google Scholar 

  3. Piatetsky-Shapiro, G., Weds, F.: Knowledge Discovery in Databases, pp. 24–27. MIT Press, Cambrige (1991)

    Google Scholar 

  4. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining, pp. 51–55. MIT Press, Cambridge (1996)

    Google Scholar 

  5. Frawley, W.J., Piatetsky-Shapiro, G., Matheus, C.J.: Knowledge Discovery in Databases: An Overview. AI Magazine, 57–70 (1992)

    Google Scholar 

  6. Thang, K.F., Aggarwal, R.K., Esp, D.G., McGrail, A.J.: Statistical and Neural Network Analysis of Dissolved Gases in Power. Dielectric Materials, Measurements and Applications Conference Publication, 324–329 (2000)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Yan-guang, C. (2012). Research on Equipment Failure Risk Control System Based on the Knowledge Integration. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_114

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  • DOI: https://doi.org/10.1007/978-3-642-34041-3_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34040-6

  • Online ISBN: 978-3-642-34041-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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