참고문헌 (54)

1. Journal of Power Sources J L Zhang 196 15 6007 2011 10.1016/j.jpowsour.2011.03.101 J. L. Zhang and J. Lee, A review on prognostics and health monitoring of li-ion battery, Journal of Power Sources, 196 (15) (2011) 6007-6014. 

   상세보기

2. IEEE Transactions on Reliability X S Si 61 1 50 2012 10.1109/TR.2011.2182221 X. S. Si, W. B. Wang, C. H. Hu, D. H. Zhou and M. G. Pecht, Remaining useful life estimation based on a nonlinear diffusion degradation process, IEEE Transactions on Reliability, 61 (1) (2012) 50-67. 

   상세보기

3. Mechanical Systems and Signal Processing X S Si 35 219 2013 10.1016/j.ymssp.2012.08.016 X. S. Si, W. B. Wang, C. H. Hu, M. Y. Chen and D. H. Zhou, A wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation, Mechanical Systems and Signal Processing, 35 (2013) 219-237. 

   상세보기

4. Applied Acoustics J B Ali 89 16 2015 10.1016/j.apacoust.2014.08.016 J. B. Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello and F. Fnaiech, Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals, Applied Acoustics, 89 (2015) 16-27. 

   상세보기

5. IEEE Transactions on Reliability L X Liao 63 1 191 2014 10.1109/TR.2014.2299152 L. X. Liao and F. Kottig, Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction, IEEE Transactions on Reliability, 63 (1) (2014) 191-207. 

   상세보기

6. IEEE Transactions on Transportation Electrification K L Liu 5 4 1225 2019 10.1109/TTE.2019.2944802 K. L. Liu, X. S. Hu, Z. B. Wei, Y. Li and Y. Jiang, Modified gaussian process regression models for cyclic capacity prediction of lithium-ion batteries, IEEE Transactions on Transportation Electrification, 5 (4) (2019) 1225-1236. 

   상세보기

7. Reliability Engineering and System Safety Z S Ye 139 58 2015 10.1016/j.ress.2015.02.005 Z. S. Ye, N. Chen and Y. Shen, A new class of wiener process models for degradation analysis, Reliability Engineering and System Safety, 139 (2015) 58-67. 

   상세보기

8. IEEE Transactions on Reliability K Medjaher 61 2 292 2012 10.1109/TR.2012.2194175 K. Medjaher, D. A. Tobon-Mejia and N. Zerhouni, Remaining useful life estimation of critical components with application to bearings, IEEE Transactions on Reliability, 61 (2) (2012) 292-302. 

   상세보기

9. Reliability Engineering and System Safety J M van Noortwijk 94 1 2 2009 10.1016/j.ress.2007.03.019 J. M. van Noortwijk, A survey of the application of gamma processes in maintenance, Reliability Engineering and System Safety, 94 (1) (2009) 2-21. 

   상세보기

10. Applied Intelligence C S Yang 46 1 227 2017 10.1007/s10489-016-0829-4 C. S. Yang, S. Letourneau, J. Liu, Q. Q. Cheng and Y. B. Yang, Machine learning-based methods for TTF estimation with application to APU prognostics, Applied Intelligence, 46 (1) (2017) 227-239. 

    상세보기

11. Reliability Engineering and System Safety T Tao 177 35 2018 10.1016/j.ress.2018.04.027 T. Tao, E. Zio and W. Zhao, A novel support vector regression method for online reliability prediction under multi-state varying operating conditions, Reliability Engineering and System Safety, 177 (2018) 35-49. 

    상세보기

12. Engineering Applications of Artificial Intelligence T Benkedjouh 26 7 1751 2013 10.1016/j.engappai.2013.02.006 T. Benkedjouh, K. Medjaher, N. Zerhouni and S. Rechak, Remaining useful life estimation based on nonlinear feature reduction and support vector regression, Engineering Applications of Artificial Intelligence, 26 (7) (2013) 1751-1760. 

    상세보기

13. Mechanical Systems and Signal Processing R Zhao 115 213 2019 10.1016/j.ymssp.2018.05.050 R. Zhao, R. Q. Yan, Z. H. Chen, K. Z. Mao, P. Wang and R. X. Gao, Deep learning and its applications to machine health monitoring, Mechanical Systems and Signal Processing, 115 (2019) 213-237. 

    상세보기

14. Journal of Manufacturing Systems L Ren 43 2 248 2017 10.1016/j.jmsy.2017.02.013 L. Ren, J. Cui, Y. Q. Sun and X. J. Cheng, Multi-bearing remaining useful life collaborative prediction: a deep learning approach, Journal of Manufacturing Systems, 43 (2) (2017) 248-256. 

    상세보기

15. IEEE Transactions on Systems Man Cybernetics-Systems J Deutsch 48 99 11 2018 10.1109/TSMC.2017.2697842 J. Deutsch and D. He, Using deep learning-based approach to predict remaining useful life of rotating components, IEEE Transactions on Systems Man Cybernetics-Systems, 48 (99) (2018) 11-20. 

    상세보기

16. 10.1109/IJCNN.2016.7727410 P. Lim, K. G. Chi and K. C. Tan, A time window neural network based framework for remaining useful life estimation, Proc. 2016 International Joint Conference on Neural Networks (IJCNN) (2016). 

    

17. IEEE Transactions on Neural Networks and Learning Systems C Zhang 28 10 2306 2017 10.1109/TNNLS.2016.2582798 C. Zhang, P. Lim, A. K. Qin and K. C. Tan, Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics, IEEE Transactions on Neural Networks and Learning Systems, 28 (10) (2017) 2306-2318. 

    상세보기

18. 10.1109/TIE.2021.3050382 Z. He, H. Shao, Z. Ding, H. Jiang and J. C. Cheng, Modified deep auto-encoder driven by multi-source parameters for fault transfer prognosis of aero-engine, IEEE Transactions on Industrial Electronics (99) (2021) 1-1. 

    상세보기

19. Reliability Engineering and System Safety X Li 172 1 2018 10.1016/j.ress.2017.11.021 X. Li, Q. Ding and J. Q. Sun, Remaining useful life estimation in prognostics using deep convolution neural networks, Reliability Engineering and System Safety, 172 (2018) 1-11. 

    상세보기

20. IEEE Transactions on Vehicular Technology Y Z Zhang 67 7 5695 2018 10.1109/TVT.2018.2805189 Y. Z. Zhang, R. Xiong, H. W. He and M. G. Pecht, Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries, IEEE Transactions on Vehicular Technology, 67 (7) (2018) 5695-5705. 

    상세보기

21. Neurocomputing Y T Wu 275 167 2018 10.1016/j.neucom.2017.05.063 Y. T. Wu, M. Yuan, S. P. Dong, L. Lin and Y. Q. Liu, Remaining useful life estimation of engineered systems using vanilla LSTM neural networks, Neurocomputing, 275 (2018) 167-179. 

    상세보기

22. 10.1007/978-3-319-32025-0_14 G. Sateesh Babu, Z. Peilin and L. Xiao, Deep convolutional neural network based regression approach for estimation of remaining useful life, Proc. of 2016 International Conference on Database Systems for Advanced Applications (DASFAA) (2016) 214-228. 

    

23. Journal of the Brazilian Society of Mechanical Sciences and Engineering C Z Wu 41 11 1 2019 10.1007/s40430-019-2010-6 C. Z. Wu, F. Z. Feng, S. J. Wu, P. C. Jiang and J. Wang, A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41 (11) (2019) 1-11. 

    상세보기

24. Proc. of 2020 Institution of Mechanical Engineers, Part O-Journal of Risk and Reliability A R T Palazuelos 234 151 2020 10.1177/1748006X19866546 A. R. T. Palazuelos, E. L. Droguett and R. Pascual, A novel deep capsule neural network for remaining useful life estimation, Proc. of 2020 Institution of Mechanical Engineers, Part O-Journal of Risk and Reliability, 234 (2020) 151-167. 

    상세보기

25. Mechanical Systems and Signal Processing B Wang 134 106330 2019 10.1016/j.ymssp.2019.106330 B. Wang, Y. G. Lei, N. P. Li and T. Yan, Deep separable convolutional network for remaining useful life prediction of machinery, Mechanical Systems and Signal Processing, 134 (2019) 106330. 

    상세보기

26. Expert Systems With Applications S Pillai 171 57 114569 2021 10.1016/j.eswa.2021.114569 S. Pillai and P. Vadakkepat, Two stage deep learning for prognostics using multi-loss encoder and convolutional composite features, Expert Systems With Applications, 171 (57) (2021) 114569. 

    상세보기

27. Journal of Computing and Information Science in Engineering X Xu 20 2 1 2020 10.1115/1.4045293 X. Xu, Q. H. Wu, X. Li and B. Q. Huang, Dilated convolution neural network for remaining useful life prediction, Journal of Computing and Information Science in Engineering, 20 (2) (2020) 1-14. 

    상세보기

28. Reliability Engineering and System Safety X Li 182 208 2019 10.1016/j.ress.2018.11.011 X. Li, W. Zhang and Q. Ding, Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction, Reliability Engineering and System Safety, 182 (2019) 208-218. 

    상세보기

29. Mechanical Systems and Signal Processing Y N Qian 83 549 2017 10.1016/j.ymssp.2016.06.031 Y. N. Qian, R. Q. Yan and R. X. Gao, A multi-time scale approach to remaining useful life prediction in rolling bearing, Mechanical Systems and Signal Processing, 83 (2017) 549-567. 

    상세보기

30. IEEE Transactions on Industrial Electronics J Zhu 66 4 3208 2019 10.1109/TIE.2018.2844856 J. Zhu, N. Chen and W. W. Peng, Estimation of bearing remaining useful life based on multiscale convolutional neural network, IEEE Transactions on Industrial Electronics, 66 (4) (2019) 3208-3216. 

    상세보기

31. Applied Soft Computing H Li 89 106113 2020 10.1016/j.asoc.2020.106113 H. Li, W. Zhao, Y. X. Zhang and E. Zio, Remaining useful life prediction using multi-scale deep convolutional neural network, Applied Soft Computing, 89 (2020) 106113. 

    상세보기

32. National Science Review H Li 5 1 24 2018 10.1093/nsr/nwx110 H. Li, Deep learning for natural language processing: advantages and challenges, National Science Review, 5 (1) (2018) 24-26. 

    상세보기

33. Journal of Manufacturing Systems J J Zhang 48 78 2018 10.1016/j.jmsy.2018.05.011 J. J. Zhang, P. Wang, R. Q. Yan and R. X. Gao, Long short-term memory for machine remaining life prediction, Journal of Manufacturing Systems, 48 (2018) 78-86. 

    상세보기

34. 10.1109/MWSCAS.2017.8053243 R. Dey and F. M. Salem, Gate-variants of gated recurrent unit (GRU) neural networks, Midwest Symp. Circuit (2017) 1597-1600. 

    

35. Neurocomputing L Guo 240 98 2017 10.1016/j.neucom.2017.02.045 L. Guo, N. P. Li, F. Jia, Y. G. Lei and J. Lin, A recurrent neural network based health indicator for remaining useful life prediction of bearings, Neurocomputing, 240 (2017) 98-109. 

    상세보기

36. Measurement M R Hou 159 15 107788 2020 10.1016/j.measurement.2020.107788 M. R. Hou, D. C. Pi and B. R. Li, Similarity-based deep learning approach for remaining useful life prediction, Measurement, 159 (15) (2020) 107788. 

    상세보기

37. Reliability Engineering and System Safety W N Yu 199 106926 2020 10.1016/j.ress.2020.106926 W. N. Yu, I. Y. Kim and C. Mechefske, An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme, Reliability Engineering and System Safety, 199 (2020) 106926. 

    상세보기

38. ISA Transactions J Wu 97 241 2020 10.1016/j.isatra.2019.07.004 J. Wu, K. Hu, Y. W. Cheng, H. P. Zhu, X. Y. Shao and Y. H. Wang, Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network, ISA Transactions, 97 (2020) 241-250. 

    상세보기

39. IEEE Transactions on Industrial Electronics C G Huang 66 11 8792 2019 10.1109/TIE.2019.2891463 C. G. Huang, H. Z. Huang and Y. F. Li, A bidirectional LSTM prognostics method under multiple operational conditions, IEEE Transactions on Industrial Electronics, 66 (11) (2019) 8792-8802. 

    상세보기

40. IEEE Transactions on Industrial Electronics R Zhao 65 2 1539 2018 10.1109/TIE.2017.2733438 R. Zhao, D. Z. Wang, R. Q. Yan, K. Z. Mao, F. Shen and J. J. Wang, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Transactions on Industrial Electronics, 65 (2) (2018) 1539-1548. 

    상세보기

41. Applied Soft Computing M Xia 93 106351 2020 10.1016/j.asoc.2020.106351 M. Xia, X. Zheng, M. Imran and M. Shoaib, Data-driven prognosis method using hybrid deep recurrent neural network, Applied Soft Computing, 93 (2020) 106351. 

    상세보기

42. IEEE Transactions on Industrial Electronics Z H Chen 68 3 2521 2021 10.1109/TIE.2020.2972443 Z. H. Chen, M. Wu, R. Zhao, F. Guretno, R. Q. Yan and X. L. Li, Machine remaining useful life prediction via an attention-based deep learning approach, IEEE Transactions on Industrial Electronics, 68 (3) (2021) 2521-2531. 

    상세보기

43. Computers in Industry A Al-Dulaimi 108 186 2019 10.1016/j.compind.2019.02.004 A. Al-Dulaimi, S. Zabihi, A. Asif and A. Mohammadi, A multimodal and hybrid deep neural network model for remaining useful life estimation, Computers in Industry, 108 (2019) 186-196. 

    상세보기

44. Journal of Computing and Information Science in Engineering A Al-Dulaimi 20 2 1 2020 10.1115/1.4045491 A. Al-Dulaimi, S. Zabihi, A. Asif and A. Mohammed, NBLSTM: noisy and hybrid convolutional neural network and BLSTM-based deep architecture for remaining useful life estimation, Journal of Computing and Information Science in Engineering, 20 (2) (2020) 1-14. 

    상세보기

45. 10.1007/s00521-021-06089-1 T. C. Wang, D. Guo and X. M. Sun, Remaining useful life predictions for turbofan engine degradation based on concurrent semi-supervised model, Neural Computing and Applications (2021) (online first). 

    상세보기

46. Sensors-Basel R Zhao 17 2 273 2017 10.3390/s17020273 R. Zhao, R. Q. Yan, J. J. Wang and K. Z. Mao, Learning to monitor machine health with convolutional bi-directional LSTM networks, Sensors-Basel, 17 (2) (2017) 273. 

    상세보기

47. Computers in Industry T B Xia 115 103182 2020 10.1016/j.compind.2019.103182 T. B. Xia, Y. Song, Y. Zheng, E. S. Pan and L. F. Xi, An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation, Computers in Industry, 115 (2020) 103182. 

    상세보기

48. 10.1109/ICCV.2015.510 D. Tran, L. Bourdev, R. Fergus, L. Torresani and M. Paluri, Learning spatiotemporal features with 3D convolutional networks, Proc. of 2015 IEEE International Conference on Computer Vision (ICCV) (2015) 4489-4497. 

    

49. 10.1109/ICPHM.2017.7998311 S. Zheng, K. Ristovski, A. Farahat and C. Gupta, Long short-term memory network for remaining useful life estimation, Proc. of 2017 IEEE International Conference on Prognostics and Health Management (ICPHM) (2017) 88-95. 

    

50. 10.1109/ICASI.2018.8394326 C. S. Hsu and J. R. Jiang, Remaining useful life estimation using long short-term memory deep learning, Proc. of 2018 IEEE International Conference on Applied System Innovation (ICASI) (2018) 58-61. 

    

51. 10.1109/PHM.2008.4711414 A. Saxena, K. Goebel, D. Simon and N. Eklund, Damage propagation modeling for aircraft engine run-to-failure simulation, Proc. of 2008 International Conference on Prognostics and Health Management (PHM) (2008) 1-9. 

    

52. Mechanical Systems and Signal Processing W N Yu 129 764 2019 10.1016/j.ymssp.2019.05.005 W. N. Yu, I. Y. Kim and C. Mechefske, Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme, Mechanical Systems and Signal Processing, 129 (2019) 764-780. 

    상세보기

53. 10.36001/ijphm.2018.v9i1.2689 N. Gugulothu, T. V. Vishnu, P. Malhotra, L. Vig, P. Agarwal and G. Shroff, Predicting remaining useful life using time series embeddings based on recurrent neural networks, International Journal of Prognostics and Health Management, 9 (1) (2018). 

    

54. Reliability Engineering and System Safety A L Ellefsen 183 240 2019 10.1016/j.ress.2018.11.027 A. L. Ellefsen, E. Bjorlykhaug, V. Æsøy, S. Ushakov and H. X. Zhang, Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture, Reliability Engineering and System Safety, 183 (2019) 240-251. 

    상세보기