Accession Number : ADP010161
Title : Automatic Machinery Fault Detection and Diagnosis Using Fuzzy Logic
Corporate Author : JOINT OIL ANALYSIS PROGRAM PENSACOLA FLTECHNICAL SUPPORT CENTER
Personal Author(s) : Mechefske, C. K. ; Del Mar, J. ; Prendergast, D.
PDF Url : ADP010161
Report Date : APR 1996
Pagination or Media Count : 8
Abstract : Machine condition monitoring incorporates a number of machinery fault diagnosis techniques. Many of these machinery fault diagnostic techniques involve automatic signal classification. In this paper Fuzzy logic techniques have been applied to classify frequency spectra presenting various bearing faults. The frequency spectra have been processed by four common Fuzzy set shapes: linear, triangular, S-curve and Pi curve. 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 six different bearing fault spectra was observed when the frequency spectra were represented by Pi curves. The degree of membership of each individual spectrum with respect to the other spectra, however, indicated a certain degree of overlapping. Further investigations must be conducted in order to optimize the ability to classify spectra with a certain degree of overlapping or masking.
Descriptors : *SYMPOSIA, *ROLLER BEARINGS, *FUZZY LOGIC, SPECTRA, AUSTRALIA.
Subject Categories : Computer Hardware
Machinery and Tools
Distribution Statement : APPROVED FOR PUBLIC RELEASE