Research on constructing a degradation index and predicting the remaining useful life for rolling element bearings of complex equipment

被引：0
作者：
Lei Zhao
Yongxiang Zhang
Jiawei Li
机构：
[1] Naval University of Engineering,
来源：
Journal of Mechanical Science and Technology | 2021年 / 35卷
关键词：
Autoregressive integrated moving average (ARIMA); Complex equipment; Fast iterative filtering decomposition (FIFD); Nonlinear autoregressive neural network (NARNN); Remaining useful life prediction; Rolling element bearing;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Due to the interference of complex transmission path and noise, the weak characteristic signal of a fault in complex equipment is difficult to extract, and then it is hard to establish an index that can timely and effectively reflect the degradation state of the bearing fault. A bearing degradation state index was constructed by using moving average coarsegrained, fast iterative filtering decomposition, permutation entropy, Wasserstein distance and cumulative sum method, which realize the recognition and evaluation of bearing degradation state. On this basis, a hybrid model based on autoregressive integral moving average and nonlinear autoregressive neural network was constructed. Experimental study showed that the model could achieve accurately the residual life prediction of bearing for complex equipment.
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页码：4313 / 4327
页数：14
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