Objective machinery fault diagnosis using fuzzy logic

Vibration-based machine condition monitoring incorporates a number of machinery fault detection and diagnostic techniques. Many machinery fault diagnostic techniques use 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 representing a number of different fault conditions 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 frequency spectra. Correct classification of different bearing fault spectra was observed when the correct combination of fuzzy set shapes and range of membership domains 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 to other classes of machinery using generic software. (C) 1998 Academic Press.