Automatic Machinery Fault Detection and Diagnosis Using Fuzzy Logic
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1998
- Accession Number
- ADA347295
Entities
People
- Chris K. Mechefske
Organizations
- Western University