Methods for diagnostics of bearings in non-stationary environment

Bearing failure is one of the major causes of breakdown in rotating machinery. One of the key challenges in bearing diagnostics and prognostics is to detect the defect as early as possible, when the failure signs are weak. Vibration based early detection of bearing failure requires improvement of the signal to noise ratio by an effective signal de-noising and extraction of the weak failure signs that can be obscured by other vibration sources and noise. The challenge is to enhance the weak signature in the early stages of defect development. The task of enhancing the weak failure signs is complicated by the fact that changes in operating conditions influence vibrations sources and change the mixture recorded by the sensors. As a result, the recorded signal becomes non-stationary. The proposed technique suggests a solution to that problem. The technique adapts the basic dephase method to analysis of non-stationary signals recorded from a system operating under changing operating conditions. The adapted dephase method was applied to vibrations measured in rotating machinery, including systems with healthy bearings and damaged bearings. The same feature extraction procedure was applied to signatures in the orders domain before and after the application of the adapted dephase. The results show the effectiveness of the method for diagnosis both in the orders representation and in the orders of the envelope representation.