Prediction of performance deterioration of rolling bearing based on JADE and PSO-SVM

In order to improve the prediction accuracy of performance degradation trends of rolling bearings, a method based on the joint approximative diagonalization of eigen-matrices (JADE) and particle swarm optimization support vector machine (PSO-SVM) was proposed. Firstly, the features of the time-domain, frequency-domain, and time-frequency-domain eigenvalues of the vibration signal corresponding to the entire life cycle of the rolling bearing are extracted, and the performance degradation parameters are initially selected by using the monotonicity parameter. Then, a fusion feature that can effectively represent the performance degradation is obtained by using the JADE method. Finally, the prediction model based on PSO-SVM is constructed to predict the performance degradation trend. By comparing with the prediction results obtained by other classical methods, it can be proved that this method can accurately predict the performance degradation trend and the remaining useful life (RUL) of rolling bearings under small sample sizes, and has considerable application potentials.

Automated summary

The method proposed in this study combines the Joint Approximative Diagonalization of Eigen-matrices (JADE) with Particle Swarm Optimization Support Vector Machine (PSO-SVM) to improve the accuracy of predicting performance degradation trends in rolling bearings. By extracting features from vibration signals and constructing a prediction model, this method accurately predicts the performance degradation trends and remaining useful life of rolling bearings.