Accession Number:

ADA347295

Title:

Automatic Machinery Fault Detection and Diagnosis Using Fuzzy Logic

Descriptive Note:

Corporate Author:

UNIVERSITY OF WESTERN ONTARIO LONDON

Personal Author(s):

Report Date:

1998-01-01

Pagination or Media Count:

10.0

Abstract:

Vibration based machine condition monitoring MCM incorporates a number of machinery fault detection and diagnostic techniques. Many of the machinery fault diagnostic techniques involve automatic signal classification in order to increase accuracy and reduce errors caused by subjective human judgment. In this paper Fuzzy logic techniques have been applied to classify frequency spectra representing various rolling element bearing faults. The frequency spectra have been processed using a variety of Fuzzy set shapes. The application of basic Fuzzy logic techniques has allowed Fuzzy numbers to be generated which represent the similarity between two frequency spectra. Correct classification of different bearing fault spectra was observed when the correct combination of Fuzzy set shapes and degree of membership criterion were used. The problem of membership overlapping found in previous studies, where classifying individual spectrum with respect to spectra that represent true fault classes was not conclusive, has been overcome. Further work is described which will extend this technique for application with other classes of machinery using generic software.

Subject Categories:

  • Cybernetics
  • Machinery and Tools

Distribution Statement:

APPROVED FOR PUBLIC RELEASE