Abstract:

In the prediction of rolling bearing degradation trend, traditional feature selection mainly relies on manual experience and a single evaluation algorithm, which is easy to cause under-selection or misselection of features. Moreover, a single deep learning network cannot fully mine the performance degradation information contained in the data, which results in low prediction accuracy of the model. To solve the above problem, a prediction method of rolling bearing degradation trend based on adaptive Mahalanobis space (AMS) and fusion deep learning network is proposed. Firstly, the original signals are decomposed and the correlated kurtosis coefficient criterion is used to screen the intrinsic mode function (IMF) to reconstruct the new signals, and the features are extracted from the multi-domain perspective. Secondly, the multi-objective feature selection algorithm based on AMS is built to optimize characteristic automatically. With the aim of reducing manual dependencies, and serengthening the adaptability and generalization, Mahalanobis distance (MD) is combined with the exponential weighted moving average (EWMA) method to well characterize the degradation trend of bearing performance. Finally, the fusion model of sparse auto encoder and gated recurrent unit (SAE-GRU) is used for prediction. The experimental results show that the proposed method can effectively screen the optimal features and significantly improve the prediction accuracy.

Key words: rolling bearing, adaptive Mahalanobis space (AMS), multi-objective feature selection, fusion model, degradation trend prediction