In recent years remaining useful life of rolling bearings is paid much more attention. In this paper, the remaining useful life prediction based on fault diagnosis is proposed. Based on the real-time fault diagnosis results of the bearing, the remaining life is predicted and a set of bearing life expectancy prediction system is established by obtaining the vibration signal. In order to solve the problem that the whole life fault data is difficult to obtain, make full use of the bearing information contained in unlabeled data and take into account the advantages of each algorithm, the remaining useful life prediction of bearing is studied based on a semi supervised co-training method. The effectiveness and prediction accuracy of this method are demonstrated by a case study.


Bearing Remaining useful life prediction Fault diagnosis Semi supervised co-training 



This work is also partly supported by State Key Lab of Rail Traffic Control & Safety (Contract No. RCS2016ZT006). This work is also partly supported by National Key R&D Program of China (Contract No. 2017YFB1201201).


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Rail Traffic Control and SafetyBeijing Jiaotong UniversityBeijingChina

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