RESEARCH PAPER
Remaining useful life prediction with insufficient degradation data based on deep learning approach
| 1 | University of Electronic Science and Technology of China Zhongshan Institute, School of Computer, Zhongshan, China, 528400 |
| 2 | Guangdong University of Technology, Guangzhou, School of Automation, China, 510006 |
| 3 | University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu 611731 |
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):745–756
HIGHLIGHTS
- Focus on the improvement of the RUL prediction effect in the case of insufficient degradation data.
- A data amplification network based on cycleGAN is designed to effectively increase the size of the degradation dataset.
- A RUL prediction framework is constructed with the sliding time window strategy and BiLSTM network.
- Experimental results show the RUL prediction performance has been significantly improved by the proposed data amplification approach.
KEYWORDS
deep learningremaining useful lifedegradation datadata amplificationcycle-consistentgenerative adversarial network
ABSTRACT
Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To
tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.
REFERENCES (36)
1.
Abdulraheem A, Abdullah Arshah R, Qin H. Evaluating the Effect of Dataset Size on Predictive Model Using Supervised Learning Technique. International Journal of Software Engineering & Computer Sciences (IJSECS) 2015; 1: 75–84, https://doi.org/10.15282/ijsec....
2.
Babu G S, Zhao P, Li X-L. Deep convolutional neural network based regression approach for estimation of remaining useful life. International conference on database systems for advanced applications, Springer: 2016: 214–228, https://doi.org/10.1007/978-3-....
3.
Deutsch J, He D. Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2017; 48(1): 11–20, https://doi.org/10.1109/TSMC.2....
4.
Graves A, Mohamed A, Hinton G. Speech Recognition with Deep Recurrent Neural Networks. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2013. doi:10.1109/ICASSP.2013.6638947, https://doi.org/10.1109/ICASSP....
5.
Guo L, Li N, Jia F et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 2017; 240: 98–109, https://doi.org/10.1016/j.neuc....
6.
KARABACAK E Yunus, GÜRSEL ÖZMEN N, GÜMÜŞEL L. Worm gear condition monitoring and fault detection from thermal images via deep learning method. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22(3): 544–556, http://dx.doi.org/10.17531/ein....
7.
Khan S, Yairi T. A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing 2018; 107: 241–265, https://doi.org/10.1016/j.ymss....
8.
Le Son K, Fouladirad M, Barros A et al. Remaining useful life estimation based on stochastic deterioration models: A comparative study. Reliability Engineering & System Safety 2013; 112: 165–175, https://doi.org/10.1016/j.ress....
9.
Li D, Chen D, Jin B et al. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. In Tetko IV, Kůrková V, Karpov P, Theis F (eds): Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, Cham, Springer International Publishing: 2019: 703–716, https://doi.org/10.1007/978-3-....
10.
Li J, Li X, He D. A directed acyclic graph network combined with cnn and lstm for remaining useful life prediction. IEEE Access 2019; 7: 75464–75475, https://doi.org/10.1109/ACCESS....
11.
Li X, Ding Q, Sun J. Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety 2018; 172: 1–11, https://doi.org/10.1016/j.ress....
12.
Li Y, Wang K. Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22(1): 63–72, http://dx.doi.org/10.17531/ein....
13.
Lyu Y, Gao J, Chen C et al. Optimal Burn-in Strategy for High Reliable Products Using Convolutional Neural Network. IEEE Access 2019; 7: 178511–178521, https://doi.org/10.1109/ACCESS....
14.
Lyu Y, Gao J, Chen C et al. Joint model for residual life estimation based on Long-Short Term Memory network. Neurocomputing 2020; 410: 284–294, https://doi.org/10.1016/j.neuc....
15.
Mao W, He J, Zuo M J. Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Transactions on Instrumentation and Measurement 2019. doi:10.1109/TIM.2019.2917735, https://doi.org/10.1109/TIM.20....
16.
Nieto P G, Garcia-Gonzalo E, Lasheras F S, de Cos Juez F J. Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering & System Safety 2015; 138: 219–231, https://doi.org/10.1016/j.ress....
17.
Peng K, Jiao R, Dong J, Pi Y. A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter. Neurocomputing 2019; 361: 19–28, https://doi.org/10.1016/j.neuc....
18.
Ragab A, Ouali M-S, Yacout S, Osman H. Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation. Journal of Intelligent Manufacturing 2016; 27(5): 943–958, https://doi.org/10.1007/s10845....
19.
Ren L, Sun Y, Wang H, Zhang L. Prediction of bearing remaining useful life with deep convolution neural network. IEEE Access 2018; 6: 13041–13049, https://doi.org/10.1109/ACCESS....
20.
Sagheer A, Kotb M. Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 2019; 323: 203–213, https://doi.org/10.1016/j.neuc....
21.
Si X-S, Wang W, Hu C-H et al. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems and Signal Processing 2013; 35(1–2): 219–237, https://doi.org/10.1016/j.ymss....
22.
Sohani A, Sayyaadi H, Hoseinpoori S. Modeling and multi-objective optimization of an M-cycle cross-flow indirect evaporative cooler using the GMDH type neural network. International Journal of Refrigeration 2016; 69: 186–204, https://doi.org/10.1016/j.ijre....
23.
Su C, Chen H, Wen Z. Prediction of remaining useful life for lithium-ion battery with multiple health indicators. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23(1): 176–183, http://dx.doi.org/10.17531/ein....
24.
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 2018. doi:10.1109/TR.2018.2882682, https://doi.org/10.1109/TR.201....
25.
Wen B, Xiao X, Wang X et al. Data-driven remaining useful life prediction based on domain adaptation. PeerJ Computer Science 7:e690 2021. doi:https://doi.org/10.7717/peerj-..., https://doi.org/10.7717/peerj-....
26.
Xie Y, Zhang T. A transfer learning strategy for rotation machinery fault diagnosis based on cycle-consistent generative adversarial networks. 2018 Chinese Automation Congress (CAC), IEEE: 2018: 1309–1313, https://doi.org/10.1109/CAC.20....
27.
Yin S, Ding S X, Xie X, Luo H. A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics 2014; 61(11): 6418–6428, https://doi.org/10.1109/TIE.20....
28.
Yinka-Banjo C, Ugot O-A. A review of generative adversarial networks and its application in cybersecurity. Artificial Intelligence Review 2020; 53(3): 1721–1736, https://doi.org/10.1007/s10462....
29.
Yoon J, Drumright L N, van der Schaar M. Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN). IEEE Journal of Biomedical and Health Informatics 2020; 24(8): 2378–2388, https://doi.org/10.1109/JBHI.2....
30.
Zhai Q, Ye Z-S. RUL prediction of deteriorating products using an adaptive Wiener process model. IEEE Transactions on Industrial Informatics 2017; 13(6): 2911–2921, https://doi.org/10.1109/TII.20....
31.
Zhang X, Xiao P, Yang Y et al. Remaining Useful Life Estimation Using CNN-XGB with Extended Time Window. IEEE Access 2019; PP: 1–1, https://doi.org/10.1109/ACCESS....
32.
Zhang Y, Xiong R, He H, Pecht M G. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology 2018; 67(7): 5695–5705, https://doi.org/10.1109/TVT.20....
33.
Zheng G, Sun W, Zhang H et al. Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23(4): 612–618, https://doi.org/10.17531/ein.2....
34.
Zhu J, Chen N, Peng W. Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics 2018; 66(4): 3208–3216, https://doi.org/10.1109/TIE.20....
35.
Zhu K, Liu T. Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Transactions on Industrial Informatics 2017; 14(1): 69–78, https://doi.org/10.1109/TII.20....
36.
Zio E, Di Maio F. A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering & System Safety 2010; 95(1): 49–57, https://doi.org/10.1016/j.ress....
CITATIONS (1):
1.
Technological Methods for Controlling the Elastic-Deformable State in Turning and Grinding Shafts of Low Stiffness
Antoni Świć, Arkadiusz Gola, Olga Orynycz, Karol Tucki, Jonas Matijošius
Materials
Antoni Świć, Arkadiusz Gola, Olga Orynycz, Karol Tucki, Jonas Matijošius
Materials
RELATED ARTICLE
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.