RESEARCH PAPER
Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning
1
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
2
Key Laboratory of Vibration and Control of Aero-Propulsion Systems of Ministry of Education, Northeastern University, Shenyang 110819, China
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):685-694
HIGHLIGHTS
- Spatial pyramid pooling extracts multi-scale degradation features of bearings.
- TL solves the inconsistent distribution of degraded data for different failed bearings.
- The SPP-CNNTL model shows a better prediction effect on the RUL of the bearing.
KEYWORDS
rolling bearingsRemaining useful life (RUL)Convolutional Neural Networks (CNN)Transfer learning (TL)
ABSTRACT
The accurate prediction of the remaining useful life (RUL) of rolling bearings is of immense importance in ensuring the safe and smooth operation of machinery and equipment. Although the prediction accuracy has been improved by a predictive model based on deep learning, it is still limited in engineering because lots of models use single-scale features to predict and assume that the degradation data of each bearing has a consistent distribution. In this paper, A deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) is proposed to obtain higher prediction accuracy with self-extraction of multi-feature from the original vibrating signal. And to consider the differences of the data distribution in different failure types, transfer learning (TL) added with maximum mean difference (MMD) measurement function is used in the RUL prediction part. Finally, the data of IEEE PHM 2012 Challenge is used for verification, and the results show that the method in this paper has high prediction accuracy
REFERENCES (32)
1.
Ali J B, Chebel-Morello B, Saidi L, Malinowski S, Fnaiech F. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing 2015; 56-57:150-172, https://doi.org/10.1016/j.ymss....
2.
Burns J E, Yao J, Chalhoub D, Chen J J, Summers R M. A Machine Learning Algorithm to Estimate Sarcopenia on Abdominal CT. Original Investigation 2020; 27(3):311-320, https://doi.org/10.1016/j.acra....
3.
Cheng H, Kong X, Chen G, Wang Q, Wang R. Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors. Measurement 2020; 168:108286, https://doi.org/10.1016/j.meas....
4.
Costa P, Akcay A, Zhang Y, Kaymak U. Remaining Useful Lifetime prediction via deep domain adaptation. Reliability Engineering & System Safety 2020; 195:106682, https://doi.org/10.1016/j.ress....
5.
Dong S, Luo T. Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement 2013; 46(9):3143–3152, https://doi.org/10.1016/j.meas....
6.
Dong S, Wen G, Lei Z, Zhang Z. Transfer learning for bearing performance degradation assessment based on deep hierarchical features. ISA Transactions 2020; 108(9):343-355, https://doi.org/10.1016/j.isat....
7.
Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V. Domain-adversarial training of neural networks. The journal of machine learning research 2016; 17(1): 2096-2030, https://dl.acm.org/doi/abs/10.....
8.
Guo L, Lei Y, Xing S, Yan T, Li N. Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics 2018; 66(9): 7316-7325, https://doi.org/10.1109/TIE.20....
9.
Hinchi A Z, Tkiouat M. Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network. Procedia Computer Science 2018; 127:123-132, https://doi.org/10.1016/j.proc....
10.
10 LeCun Y, Boser B, Denker J S, Henderson D, Howard R E, Hubbard W, Jackel L D. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation 1989; 1(4): 541–551, https://doi.org/10.1162/neco.1....
11.
Lei Y, Li N, Gontarz S, Jing L, Radkowski S, Dybala J. A model-based method for remaining useful life prediction of machinery. IEEE Transactions on reliability 2016; 65(3): 1314-1326, https://doi.org/10.1109/TR.201....
12.
Lei Y, Li N, Guo L, Li N, Yan T, Jing L. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing 2018; 104:799-834, https://doi.org/10.1016/j.ymss....
13.
Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Matti P. Deep Learning for Generic Object Detection: A Survey. International Journal of Computer Vision 2020; 128: 261–318, https://doi.org/10.1007/s11263....
14.
Lu W, Liang B, Cheng Y, Meng D, Yang J, Zhang T. Deep model based domain adaptation for fault diagnosis. IEEE Transactions on Industrial Electronics 2016; 64(3):2296-2305, https://doi.org/10.1109/TIE.20....
15.
Mao W, He J, Ming J Z. Predicting Remaining Useful Life of Rolling Bearing Based on Deep Feature Representation and Transfer Learning. IEEE Transactions on Instrumentation and Measurement 2019; 69(4):1594-1608, https://doi.org/10.1109/TIM.20....
16.
Nectoux P, Gouriveau R, Medjaher K, Ramasso E, Varnier C. PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In: IEEE International Conference on Prognostics and Health Management. Denver, CO, USA, 1-8, 2012, https://hal.archives-ouvertes. fr/hal-00719503.
17.
Pan S J, Tsang I W, Kwok J T, Yang Q. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks 2011; 22(2):199–210, https://doi.org/10.1109/TNN.20....
18.
Rai A, Upadhyay S H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International 2016; 96:289-306, https://doi.org/10.1016/j.trib....
19.
Rai A, Upadhyay S H. Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 2018; 232(6):1118-1132, https://doi.org/10.1177/095440....
20.
Salakhutdinov R, Hinton G. An Efficient Learning Procedure for Deep Boltzmann Machines. Neural Computation 2012; 24(8): 1967–2006, https://doi.org/10.1162/NECO_a....
21.
Su C, Li L, Wen Z. Remaining useful life prediction via a variational autoencoder and a time‐window‐based sequence neural network. Quality and Reliability Engineering International 2020; 36(5): 1639-1656, https://doi.org/10.1002/qre.26....
22.
Tang Y, Chen M, Wang C, Luo L, Zou X. Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review. Frontiers in Plant Science 2020; 11:510, https://doi.org/10.3389/fpls.2....
23.
Wang B, Lei Y, Li N, Li N. A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings. IEEE Transactions on Reliability 2020; 69(1):401-412, https://doi.org/10.1109/TR.201....
24.
Wang B, Lei Y, Yan T, Li N, Guo L. Recurrent convolutional neural network: A new framework for remaining useful prediction of machinery. Neurocomputing 2020; 379:117-129, https://doi.org/10.1016/j.neuc....
25.
Wang F K, Mamo T. Hybrid approach for remaining useful life prediction of ball bearings. Quality and Reliability Engineering International 2019; 35(7): 2494-2505, https://doi.org/10.1002/qre.25....
26.
Wang Q, Zhao B, Ma H, Chang J, Mao G. A method for rapidly evaluating reliability and predicting remaining useful life using twodimensional convolutional neural network with signal conversion. Journal of Mechanical Science and Technology 2019; 33:2561–2571, https://doi.org/10.1007/s12206....
27.
Wang Y, Peng Y, Zi Y, Jin X, Tsui K L. A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem. IEEE Transactions on Industrial Informatics 2016; 12(3): 924-932, https://doi.org/10.1109/TII.20....
28.
Ye Z S, Xie M. Stochastic modelling and analysis of degradation for highly reliable products. Applied Stochastic Models in Business and Industry 2015; 31(1):16-32, https://doi.org/10.1002/asmb.2....
29.
Zhang J, Wang P, Yan R, Gao R X. Long short-term memory for machine remaining life prediction. Journal of Manufacturing Systems 2018; 48(C): 78-86, https://doi.org/10.1016/j.jmsy....
30.
Zhang Y, Yang S, Li P, Hu X, Wang H. Marginalized Stacked Denoising Autoencoder with Adaptive Noise Probability for Cross Domain Classification. IEEE Access 2019; 7:2169-3536, https://doi.org/10.1109/ACCESS....
31.
Zhu J, Chen N, Peng W. Estimation of Bearing Remaining Useful Life based on Multiscale Convolutional Neural Network. IEEE Transactions on Industrial Electronics 2019; 66(4):3208-3216, https://doi.org/10.1109/TIE.20....
32.
Zhu J, Chen N, Shen C. A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mechanical Systems and Signal Processing 2020; 139: 106602, https://doi.org/10.1016/j.ymss....
CITATIONS (7):
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
