Self-driven Independent Degradation Trajectory Construction and Remaining Life Gray Prediction for Bearings
To address the problems of individual variability of bearing degradation trajectories and artificial subjectivity of degradation stage division,a self-monitoring data-driven extraction method of independent bearing degradation trajectory and an autonomous segmentation technique of degradation stages were proposed.A multiscale residual deep convolutional autoencode was developed herein to au-tonomously extract the bearing performance degradation features by unsupervised learning of bearing's own historical monitoring data,and then combined with support vector data description model to construct single bearing independent degradation trajectory.The de-trending super-threshold waveform method was introduced to automatically detect the starting degradation point,and the fail-ure threshold was set autonomously using logistic regression-based failure probability statistics meth-od,thus the bearing independent degradation trajectory was adaptively segmented.Driven by degrada-tion stage index obtained from trajectory segmentation,the accurate prediction of bearing life was achieved by combining full time power gray prediction model.The experimental results show that the multiscale residual deep convolutional autoencode network proposed herein may construct a degrada-tion trajectory reflecting the degradation law of the bearing itself according to the respective working conditions of the bearings,and the adaptive degradation trajectory segmentation method proposed herein may detect the starting degradation point and the failure threshold of the bearing without refer-ences.The results may improve the scientific objectivity of bearing degradation assessment and the en-gineering operability of life prediction.
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