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Fuzzy-Statistical Reasoning in Fault Diagnosis

  • Dan Stefanoiu
  • Florin Ionescu
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

When searching for faults threatening a system, the human expert is sometimes performing an amazingly accurate analysis of available information, frequently by using only elementary statistics. Such reasoning is referred to as “fuzzy reasoning,” in the sense that the expert is able to extract and analyse the essential information of interest from a data set strongly affected by uncertainty. Automating the reasoning mechanisms that represent the foundation of such an analysis is, in general, a difficult attempt, but also a possible one, in some cases. The chapter introduces a nonconventional method of fault diagnosis, based upon some statistical and fuzzy concepts applied to vibrations, which intends to automate a part of human reasoning when performing the detection and classification of defects.

Keywords

Root Mean Square Fault Diagnosis Fuzzy Relation Outer Race Defect Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • Dan Stefanoiu
    • 1
  • Florin Ionescu
    • 2
  1. 1.Department of Automatic Control and Computer Science“Politechnica” University of BucharestBucharestRomania
  2. 2.Department of MechatronicsUniversity of Applied Sciences in KonstanzKonstanzGermany

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