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A Hybrid Approach for Predicting the Remaining Useful Life of bearings Based on the RReliefF Algorithm and Extreme Learning Machine

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1405-1427页

作者： Sen-Hui Wang Xi Kang Cheng Wang Tian-Bing Ma Xiang He Ke Yang School of Mechatronics Engineering Anhui University of Science and TechnologyHuainan232001China Institute of Energy Hefei Comprehensive National Science CenterHefei230031China

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining *** research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected *** study proposes a hybrid predictive model to assess the RUL of rolling element *** proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain *** hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection ***,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal *** model is trained by optimizing its parameters via the grid search *** training datasets are adjusted to make them most suitable for the regression model using the cross-validation *** proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing *** comparison is constructed with other traditional *** experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

关键词： bearing degradation remaining useful life estimation RReliefF feature selection extreme learning machine

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