Bearing faults classification based on wavelet transform and artificial neural network

The most common types of induction rotating machine failures are the mechanical faults induced by misalignment, mechanical imbalance and bearing fault. It is well known that the vibration is the best and the earliest indicator of arising mechanical defect. Thus, this paper presents a novel practical bearing fault diagnosis method based on wavelet package decomposition (WPD) associated with neural network. Firstly, the raw signal is segmented by the use of WPD to a set of sub-signals (coefficients futures). Then, the energy related to the most sensible coefficients that contained the greatest dominant fault information is selected as a distinctive feature fault. The analysis results show that this fault indicator varies under different loads and states (healthy or defective). In order to automate the detection and the location of bearing defect, this feature can be used as an input to the artificial neural network. The proposed approach is capable of discriminating faults from four conditions of rolling bearing, the healthy bearing and the three different types of defected bearings: outer race, inner race, and ball. The experimental results prove the effectiveness of this approach.