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Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model

机译:基于PCHIP-EEMD-GM(1,1)模型的滚动轴承剩余的寿命预测方法

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摘要

A trend prediction method based on the Pchip-EEMD-GM(1,1) to predict the remaining useful life (RUL) of rolling bearings was proposed in this paper. Firstly, the dimension of the extracted features was reduced by the KPCA dimensionality reduction method, and the WPHM model parameters were estimated via the kernel principal components. Secondly, the hazard rate was calculated at each time, and the Pchip interpolation method was used to obtain the uniformly spaced interpolation data series. Then the main trend of signal was obtained through the EEMD method to fit the GM(1,1) prediction model. Finally, the GM (1,1) method was used to predict the remaining life of the rolling bearing. The full life test of rolling bearing was provided to demonstrate that the method predicting the hazard data directly has the higher accuracy compared with predicting the covariates, and the results verified the feasibility and effectiveness of the proposed method for predicting the remaining life.
机译:本文提出了一种基于Pchip-EEMD-GM(1,1)来预测滚动轴承剩余使用寿命(RUL)的趋势预测方法。首先,通过KPCA维数减少方法减少了提取的特征的尺寸,通过内核主成分估计WPHM模型参数。其次,每次计算危险率,并且使用PCHIP插值方法获得均匀间隔的内插数据序列。然后通过EEMD方法获得信号的主要趋势以适合GM(1,1)预测模型。最后,使用GM(1,1)方法来预测滚动轴承的剩余寿命。提供了滚动轴承的全寿命试验,以证明预测危险数据的方法直接具有更高的准确性,与预测协变量相比,结果验证了提出的方法预测剩余寿命的可行性和有效性。

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