Abstract
Aiming at the problem that the early vibration signal has a weak decay characteristic in the prediction of the remaining life of the rolling bearing, a method for optimizing the bearing residual life prediction based on the optimized ensemble empirical mode decomposition (EEMD) is proposed. First, the eigenmode decomposition of the vibration signal is performed. The effect depends on two important parameters: the average number of times and the size of the added noise. Therefore, white noise criteria are added to the set of empirical mode decomposition. Then, the decomposed intrinsic mode function (IMF) is filtered with the gray correlation degree of the envelope spectrum to filter out IMF components with decay characteristics and reconstruct signals. Finally, Multi-feature parameter vector of the reconstructed signal, its redundancy is removed by principal component analysis (PCA), and then input neural network to predict bearing residual life. Experiments show that the proposed method has higher prediction accuracy and stability.
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Wu, T., Gao, C., Fu, Z. (2019). Prediction of Remaining Life of Rolling Bearing Based on Optimized EEMD. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 528. Springer, Singapore. https://doi.org/10.1007/978-981-13-2288-4_23
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DOI: https://doi.org/10.1007/978-981-13-2288-4_23
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