Bearing remaining useful life prediction based on optimized support vector regression model with denoising technique

Bearings play an irreplaceable role in various categories of high-precision mechanical equipment. To reduce the adverse impact caused by sudden bearing failures, this paper proposes a method based on improved particle swarm optimization (IPSO), empirical mode decomposition (EMD), and support vector regression (SVR) to predict the remaining useful life (RUL) of bearing. The EMD method decomposes the raw vibration signal, and the components are chosen out according to the kurtosis criterion to reconstruct the vibration signal. After filtering out the noise, the features in time-domain and frequency-domain are extracted from the reconstructed vibration signal. The IPSO method is used to optimize three important parameters of the SVR model. Then the SVR model is trained with the extracted features to predict the bearing RUL. Actual bearing failure dataset is used to verify the validity of this method. Three different methods are used to compare with this proposed method. The results indicate that this proposed method performs best among the four methods in different evaluation indexes. The proposed method makes sense for bearing failure prediction.