A Framework for Predicting Remaining Useful Life Curve of Rolling Bearings Under Defect Progression Based on Neural Network and Bayesian Method

In order to improve Remaining Useful Life (RUL) prediction accuracy for rolling bearings under defect progressing, the robustness for individual differences and the fluctuation of vibration features are challenging issues. In this research, we propose a novel RUL prediction framework based on a Convolutional Neural Network (CNN) and Hierarchical Bayesian Regression (HBR) for considering the degradation conditions and individual differences of RUL to improve the prediction accuracy. The characteristics of the proposed framework are: (1) In order to reduce the effect of the fluctuation of vibration features, the proposed framework uses an intermediate variable indicating the degradation condition instead of predicting RUL from vibration features. (2) The proposed framework considers not only present but also past degradation conditions in CNN. We conducted the experiment on rolling bearings under defect progression and evaluated the RUL prediction accuracy of the proposed framework. The proposed framework can generate a monotonous RUL prediction curve with a probability distribution and improve the RUL prediction accuracy under defect progression.

[1]  Steven Y. Liang,et al.  Diagnostics and prognostics of a single surface defect on roller bearings , 2000 .

[2]  Mohamed Tkiouat,et al.  Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network , 2018 .

[3]  Lun Ma,et al.  Research on Condition Monitoring of Bearing Health Using Vibration Data , 2012 .

[4]  Nagi Gebraeel,et al.  Degradation modeling and monitoring of truncated degradation signals , 2012 .

[5]  Brigitte Chebel-Morello,et al.  PRONOSTIA : An experimental platform for bearings accelerated degradation tests. , 2012 .

[6]  Qiang Miao,et al.  Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators , 2018, IEEE Access.

[7]  Yong Qin,et al.  Remaining useful life prediction of rolling element bearings based on health state assessment , 2016 .

[8]  Rong Li,et al.  Residual-life distributions from component degradation signals: A Bayesian approach , 2005 .

[9]  Konstantinos Gryllias,et al.  Unsupervised Domain Adaptation based Remaining Useful Life Prediction of Rolling Element Bearings , 2020 .

[10]  Liang Guo,et al.  Machinery health indicator construction based on convolutional neural networks considering trend burr , 2018, Neurocomputing.

[11]  Weiwen Peng,et al.  Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network , 2019, IEEE Transactions on Industrial Electronics.

[12]  T. A. Harris,et al.  Fatigue Life: Basic Theory and Rating Standards , 2006 .

[13]  Masayuki Numao,et al.  Remaining Useful Life Curve Prediction of Rolling Bearings Under Defect Progression Based on Hierarchical Bayesian Regression , 2020, ECAI.

[14]  Pavan Kumar Kankar,et al.  A Review on Prognosis of Rolling Element Bearings , 2011 .

[15]  Jehn-Ruey Jiang,et al.  Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life , 2019, Sensors.

[16]  Jun-Geol Baek,et al.  A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network , 2018, Applied Sciences.

[17]  Jay Lee,et al.  Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.

[18]  David He,et al.  Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[19]  Madhav Mishra.  Prognostics and Health Management of Engineering Systems for Operation and Maintenance Optimisation , 2018 .

[20]  Yaguo Lei,et al.  Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery , 2020, Neurocomputing.

[21]  Yaguo Lei,et al.  Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .

[22]  Lei Ren,et al.  Multi-scale Dense Gate Recurrent Unit Networks for bearing remaining useful life prediction , 2019, Future Gener. Comput. Syst..

[23]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[24]  M. Hicks,et al.  Rainfall-induced slope collapse with coupled material point method , 2018 .

[25]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[26]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[27]  A. Palmgren,et al.  Dynamic capacity of rolling bearings , 1947 .

[28]  B. Wanga,et al.  Rainfall-induced slope collapse with coupled material point method , 2018 .