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.
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Stefanoiu, D., Ionescu, F. (2006). Fuzzy-Statistical Reasoning in Fault Diagnosis. In: Palade, V., Jain, L., Bocaniala, C.D. (eds) Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-631-5_5
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DOI: https://doi.org/10.1007/978-1-84628-631-5_5
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