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Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective

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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

Part of the book series: Massive Computing ((MACO,volume 6))

Abstract

The ability to acquire knowledge from empirical data or the environment is an important requirement in better understanding many natural and artificial organisms. This ability relies heavily on the quality of the raw information available about the target system. In reality, these raw information/data may contain uncertainty and fuzziness, that is, it may be imprecise or incomplete. A number of techniques, such as the Dempster-Shafer theory of belief functions and fuzzy set theory, have been developed to handle knowledge acquisition in environments that exhibit uncertainty and fuzziness. However, the advent of the rough set theory in the early 80’s provides a novel and promising way of dealing with vagueness and uncertainty. This chapter will address the issue systematically by covering a broad area including knowledge acquisition / extraction, uncertainty in general, and techniques for handling uncertainty. The basic notions of rough set theory as well as some recent applications are also included. Two simple case studies related to fault diagnosis in manufacturing systems a reused to illustrate the concepts presented in this chapter.

Triantaphyllou, E. and G. Felici (Eds.), Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques, Massive Computing Series, Springer, Heidelberg, Germany, pp. 359–394, 2006.

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Authors and Affiliations

  1. School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798

    Lian-Yin Zhai & Li-Pheng Khoo

  2. Faculty of Engineering & Surveying, University of Southern Queensland, Toowoomba, Qld, 4350, Australia

    Sai-Cheong Fok

Authors
  1. Lian-Yin Zhai
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  2. Li-Pheng Khoo
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  3. Sai-Cheong Fok
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Editors and Affiliations

  1. Louisiana State University, Baton Rouge, Louisiana, USA

    Evangelos Triantaphyllou

  2. Consiglio Nazionale delle Ricerche, Rome, Italy

    Giovanni Felici

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Zhai, LY., Khoo, LP., Fok, SC. (2006). Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective. In: Triantaphyllou, E., Felici, G. (eds) Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. Massive Computing, vol 6. Springer, Boston, MA . https://doi.org/10.1007/0-387-34296-6_11

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