Performance Prediction Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy and Extreme Learning Machine

Abstract

Performance prediction is significant to monitor the health status of rolling bearings, which can greatly reduce the loss caused by potential faults in the whole life cycle of rolling bearings. It is a very important part of Prognostic and Health Management (PHM). In this article, a new performance degradation prediction (HMEPEM) method based on high-order differential mathematical morphology gradient spectrum entropy (HOMMSE), phase space reconstruction, and extreme learning machine (ELM) is proposed to predict the performance degradation trend of rolling bearings. In the proposed HMEPEM method, the HOMMSE method is used to extract the initial features of performance degradation from the raw bearing vibration signals and divide working stages. Then the phase space reconstruction is used to further extract more useful features from the initial features of performance degradation in order to construct a feature matrix, which is input into the ELM in order to build the performance degradation prediction model for predicting the performance degradation trend of rolling bearings. The proposed HMEPEM method is validated on the performance degradation data of rolling bearings provided by the PRONOSTIA platform. The results show that the proposed HMEPEM method can efficiently track the evolution of degradation and predict the performance degradation trend of rolling bearings.