Bearing degradation process prediction based on the PCA and optimized LS-SVM model

The main steps of the proposed procedure for bearing degradation process prediction. [Display omitted] •The time and time–frequency domain methods are used to process the signal.•The more sensitive features is extracted by PCA method.•Use the phase space reconstruction method to determine the SVM model parameters.•Construct the SVM model to achieve multi-steps life prediction. Bearing degradation process prediction is extremely important in industry. This paper proposed a new method to achieve bearing degradation prediction based on principal component analysis (PCA) and optimized LS-SVM method. Firstly, the time domain, frequency domain, time–frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique PCA is used to merge the original features and reduce the dimension, the typical sensitive features are extracted. Then, based on the extracted features, the LS-SVM model is constructed and trained for bearing degradation process prediction. The pseudo nearest neighbor point method is used to determine the input number of the model. The particle swarm optimization (PSO) is used to selected the LS-SVM parameters. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology.