Ensemble empirical mode decomposition energy moment entropy and enhanced long short-term memory for early fault prediction of bearing

Abstract

Bearings are the core components of rotating machinery and are vulnerable to failure. Early fault prediction is a significant and challenging task for bearing due to the weakness of fault signal. To address this issue, a method based on enhanced long short-term memory and ensemble empirical mode decomposition energy moment entropy is proposed in this paper. First, the ensemble empirical mode decomposition is adopted to process the raw vibration signals. Intrinsic mode functions with sensitive features are selected based on the correlation coefficient and maximal information coefficient. The energy moment entropy is designed as the performance index to depict bearing performance degradation. Secondly, an enhanced long short-term memory is developed by virtue of quantum superposition principle to predict the early fault of bearing. Finally, the effectiveness and the superiority of the proposed method are validated on the bearing datasets in comparison with other existed methods.