Paper Title:
A Neuron-Fuzzy Technique for Fault Diagnosis in Rotating Machinery
  Abstract

The on-line fault diagnostics technology for machines is fast emerging for the detection of incipient faults as to avoid the unexpected failure. On the basis of fault diagnosis theory and method, this paper presents a applications of techniques for fault detection and classification in rotating machinery based on fuzzy theory and neural network theory, the basic structure and working principle of the fault intelligent diagnosis system are introduced, the knowledge stored in the neuron-fuzzy system has been extracted by a fuzzy rule set with an acceptable degree of interpretability, the model of fuzzy fault diagnosis and the self-study principle are described. The practice proves that this is an effective method of large-scale and complicated electronic equipment, and it can also be applied to other fault diagnosis of complex systems and has certain portability.

  Info
Periodical
Advanced Materials Research (Volumes 204-210)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
2188-2191
DOI
10.4028/www.scientific.net/AMR.204-210.2188
Citation
Z. Yao, Q. X. Zhao, "A Neuron-Fuzzy Technique for Fault Diagnosis in Rotating Machinery", Advanced Materials Research, Vols. 204-210, pp. 2188-2191, 2011
Online since
February 2011
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Price
$32.00
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