Abstract
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.
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References
Jianwei Y (2015) Fault diagnosis of railway bearing based on FIR-wavelet packet and LVQ neural network. Open Autom Control Syst J 7:303–313
Oppenheimer CH, Loparo KA (2002) Physically based diagnosis and prognosis of cracked rotor shafts. In Aerosense international society for optics and photonics
Zio E, Maio FD (2010) A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliab Eng Syst Safety 95(1):49–57
Hu C (2015) A co-training-based approach for prediction of remaining useful life utilizing both fault and suspension data. Mech Syst Signal Process 62–63:75–90
Zhou Z (2013) Disagreement-based semi-supervised learning. Acta Automatica Sinica 39(11)
Sikorska JZ, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25:1803–1836
Chen Yuhao (2010) Review of neural network BP algorithm. Comput Knowl Technol 6(36):10364–10365
Dvaid M, Alexnader F (2004) Active set support vector regression. IEEE Trans Neural Netw 15(2)
Acknowledgements
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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Yan, D., Wei, X. (2018). RUL Prediction for Bearings Based on Fault Diagnosis. In: Jia, L., Qin, Y., Suo, J., Feng, J., Diao, L., An, M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering, vol 482. Springer, Singapore. https://doi.org/10.1007/978-981-10-7986-3_102
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DOI: https://doi.org/10.1007/978-981-10-7986-3_102
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