Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing

As a key component in electromechanical systems, the health condition monitoring of rolling bearings is crucial for the safe operation of the whole system. For this purpose, the prediction of rolling bearing performance degradation is indispensable. To improve the accuracy of Extreme Learning Machine (ELM) based algorithms for the bearing performance degradation prediction, a novel Graph embedded ELM autoencoder (GEELM-AE) is first constructed via combining the graph embedding framework and then a performance degradation prediction method of Multilayer-Graph-embedded ELM (MGEELM) with a deep framework is developed by stacking mul-tiple GEELM-AEs. The MGEELM algorithm not only extracts the abstract features in the data by virtue of its deep structure, but also maintains the local structural information and non-local structural information of the data during feature extraction. Accordingly, the performance degradation trend of rolling bearings can be accurately predicted with the proposed MGEELM. Moreover, the MGEELM algorithm does not require reverse fine-tuning, which greatly reduces the training time and improves the prediction efficiency. The advantages of the proposed method are validated by rolling bearing life-cycle vibration data. In the experimental analysis, the four perfor-mance indicators of the proposed method obviously outperform the comparative methods, which indicates the superior ability of the proposed method in tracking the bearing operating condition evolution.

Automated summary

The health condition monitoring of rolling bearings is crucial for the safe operation of electromechanical systems. In order to improve the accuracy of bearing performance degradation prediction, a novel Graph embedded ELM autoencoder (GEELM-AE) is constructed by combining the graph embedding framework, and a Multilayer-Graph-embedded ELM (MGEELM) method is developed by stacking multiple GEELM-AEs. The proposed MGEELM accurately predicts the performance degradation trend of rolling bearings and reduces training time and improves prediction efficiency.