Automation of fault diagnosis of bearing by application of fuzzy inference system (FIS)

This work deals with the application of the fuzzy logic to automate diagnosis of bearing defects in rotating machines based on vibration signals. The classification tool used is a fuzzy inference system (FIS) of Mamdani type. The vector form of input contains parameters extracted from the signals collected from the test bench studied. The output vector contains the classes for the different operating modes of the experimental study. The results show that; pretreatment data (filtering, decimation,...), the choice of parameters of fuzzy inference system (input variables and output, types and parameters of membership functions associated with different input and output variables of the system, the generation of fuzzy inference rules,...) are of major importance for the performance of fuzzy inference system used as a tool for fault diagnosis of rotating machinery.