An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Qiaoping Tian,Honglei Wang

doi:10.3390/app10010346

Abstract

The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

Highlights

Rolling bearings are one of the key components supporting rotating shafts in rotating mechanical equipment
We propose a three-stage feature selection method based on fast correlation-based filter (FCBF)-approximate Markov Blanket (AMB) and Maximal Information Coefficient (MIC), which reduces feature redundancy and reduces feature data dimension based on the bearing degradation indicator subsets fusion method
To reflect the advantages of constructing the bearing degradation indicator set by using the three-stage feature selection method proposed in this paper, different bearings selection methods were applied to the bearings under three different working conditions to construct the bearing degradation indicator set

Summary

Introduction

Rolling bearings are one of the key components supporting rotating shafts in rotating mechanical equipment. Any accidental failure of a bearing may cause have various negative effects [3,4] ranging from production downtime to casualties or even catastrophic environmental pollution. To address these issues, online detection of bearing health is urgently required to effectively enhance the safety of mechanical equipment operation [5,6,7], predict bearing remaining useful life (RUL), and to implement an action plan to prevent catastrophic events and extend the bearing life cycle [7].

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Results

Conclusion