Rolling element-bearing feature extraction based on combined wavelets and quantum-behaved particle swarm optimization

A new approach is proposed to quickly determine the optimal band-pass filter for the resonant demodulation of rolling element bearings. The combined wavelet filter is joined with quantum-behaved particle swarm optimization for optimal filtering. The correlated kurtosis is used as an evaluation index to identify the optimal filtered signal. The proposed approach possesses good band-pass filtering properties and improves optimal filtering speed and effect to converge rapidly to an optimal solution. Simulation and experiment results show that this approach can generate fault features that are superior to those produced with the classic algorithm. It also shortens the time spent on the same filtering effect in comparison.