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.

This paper presents the application of new time frequency method, ensemble empirical modedecomposition (EEMD), in purpose to detect localized faults of damage at an early stage.EEMD is a self adaptive analysis method for non-linear and non-stationary signals and itwas recently proposed by Huang and Wu to overcome the drawbacks of the traditionalempirical mode decomposition (EMD). The vibration signal is usually noisy. To detect thefault at an early stage of its development, generally the residual signal is used. Thereexist different methods in literature to calculate the residual signal, in this paper wemention some of them and we propose a new method which is based on EEMD. The results givenby the different methods are compared by using simulated and experimental signals.