Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

Xiang Li; Yixiao Xu; Naipeng Li; Bin Yang; Yaguo Lei

doi:10.1109/JAS.2022.105935

Volume 10 Issue 1

Jan. 2023

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(1): 121-134

X. Li, Y. X. Xu, N. P. Li, B. Yang, and Y. G. Lei, “Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 121–134, Jan. 2023. doi: 10.1109/JAS.2022.105935

Citation:

X. Li, Y. X. Xu, N. P. Li, B. Yang, and Y. G. Lei, “Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 121–134, Jan. 2023. doi: 10.1109/JAS.2022.105935

Citation:

PDF( 2711 KB)

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

doi: 10.1109/JAS.2022.105935

Xiang Li^1
,,
Yixiao Xu^1
,,
Naipeng Li^{1
,
,},
Bin Yang^1
,,
Yaguo Lei^1
,

Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China

Funds: This work was supported by the National Science Fund for Distinguished Young Scholars of China (52025056), and Fundamental Research Funds for the Central Universities (xzy012022062)

More Information

Author Bio:
Xiang Li (Member, IEEE) received the B.S. degree in engineering mechanics and the Ph.D. degree in mechanics from Tianjin University in 2012 and 2017, respectively. He is currently an Associate Professor with School of Mechanical Engineering, Xi’an Jiaotong University. Prior to joining Xi’an Jiaotong University, he was a Postdoctoral Fellow in the Center for Intelligent Maintenance Systems (IMS) at the University of Cincinnati, US. He was a Visiting Scholar with University of California at Merced, US in 2015. His research interests include industrial AI, industrial big data, deep learning, fault diagnosis, prognostics, etc

Yixiao Xu received the B.E degree in School of Mechanical Engineering and Automation from Fuzhou University in 2020. He is currently pursuing the master degree in mechanical engineering at the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University. His research interests include intelligent fault diagnosis and prognosis of machines

Naipeng Li (Member, IEEE) is currently an Assistant Professor of mechanical engineering at Xi’an Jiaotong University. He received the B.S. degree in mechanical engineering from Shandong Agricultural University in 2012, and the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University in 2019. He was also a Visiting Scholar of Georgia Institute of Technology, USA. His research interests include machinery condition monitoring, intelligent fault diagnostics and remaining useful life prediction of rotating machinery

Bin Yang (Student Member, IEEE) received the B.S. and M.S. degrees in mechatronics engineering from Chang’an University in 2014 and 2017, respectively, and Ph.D. degree in mechanical engineering from Xi’an Jiaotong University in 2022. He is currently an Assistant Professor of mechanical engineering at Xi’an Jiaotong University. He was a visiting Ph.D. student of the Centre of Maintenance Optimization and Reliability Engineering (C-MORE) at the University of Toronto, Canada. His research interests include intelligent fault diagnosis of machines

Yaguo Lei (Senior Member, IEEE) received the B.S. and Ph.D. degrees in mechanical engineering from Xi’an Jiaotong University in 2002 and 2007, respectively. He is currently a Full Professor of mechanical engineering at Xi’an Jiaotong University. Prior to joining Xi’an Jiaotong University in 2010, he was a Postdoctoral Research Fellow with the University of Alberta, Canada. He was also an Alexander von Humboldt Fellow with the University of Duisburg-Essen, Germany. His research interests intelligent fault diagnosis and remaining useful life prediction.Prof. Lei is a Fellow of ASME, IET and ISEAM, and a Senior Member of IEEE. He is currently an Associate Editor or Editorial Board Member of more than ten journals, including IEEE Transactions on Industrial Electronics and Mechanical Systems and Signal Processing
Corresponding author: Naipeng Li, e-mail: naipengli@mail.xjtu.edu.cn
Received Date: 2022-06-29
Accepted Date: 2022-08-18

Available Online: 2022-08-23

Abstract

Abstract

In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing data-fusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potential sensor malfunctions, which are likely to occur in real industries especially for machines in harsh operating environments. In this paper, a deep learning-based remaining useful life (RUL) prediction method is proposed to address the sensor malfunction problem. A global feature extraction scheme is adopted to fully exploit information of different sensors. Adversarial learning is further introduced to extract generalized sensor-invariant features. Through explorations of both global and shared features, promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions. The experimental results suggest the proposed approach is well suited for real industrial applications.
- Adversarial training,
- data fusion,
- deep learning,
- remaining useful life (RUL) prediction,
- sensor malfunction

FullText(HTML)

References(49)

References

[1]	Z. Pang, X. Si, C. Hu, and Z. Zhang, “An age-dependent and statedependent adaptive prognostic approach for hidden nonlinear degrading system,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 907–921, 2022.
[2]	C. Chen, N. Lu, B. Jiang, and C. Wang, “A risk-averse remaining useful life estimation for predictive maintenance,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 412–422, 2021.
[3]	W. Wang, Y. Lei, T. Yan, N. Li, and A. Nandi, “Residual convolution long short-term memory network for machines remaining useful life prediction and uncertainty quantification,” Journal of Dynamics,Monitoring and Diagnostics, vol. 1, no. 1, pp. 2–8, 2021.
[4]	J. Wu, Y. Su, Y. Cheng, X. Shao, C. Deng, and C. Liu, “Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system,” Applied Soft Computing, vol. 68, pp. 13–23, 2018. doi: 10.1016/j.asoc.2018.03.043
[5]	X. Fang, K. Paynabar, and N. Gebraeel, “Multistream sensor fusionbased prognostics model for systems with single failure modes,” Reliability Engineering &System Safety, vol. 159, pp. 322–331, 2017.
[6]	W. Zhang, X. Li, H. Ma, Z. Luo, and X. Li, “Federated learning for machinery fault diagnosis with dynamic validation and self-supervision,” Knowledge-Based Systems, vol. 213, p. 106679, 2021.
[7]	L. Guo, N. Li, F. Jia, Y. Lei, and J. Lin, “A recurrent neural network based health indicator for remaining useful life prediction of bearings,” Neurocomputing, vol. 240, pp. 98–109, 2017. doi: 10.1016/j.neucom.2017.02.045
[8]	W. Zhang, X. Li, H. Ma, Z. Luo, and X. Li, “Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions,” Reliability Engineering & System Safety, vol. 211, p. 107556, 2021.
[9]	R. Huang, L. Xi, X. Li, C. R. Liu, H. Qiu, and J. Lee, “Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods,” Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 193–207, 2007. doi: 10.1016/j.ymssp.2005.11.008
[10]	J. Wei, G. Dong, and Z. Chen, “Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression,” IEEE Trans. Industrial Electronics, vol. 65, no. 7, pp. 5634–5643, 2018. doi: 10.1109/TIE.2017.2782224
[11]	W. Zhang, X. Li, H. Ma, Z. Luo, and X. Li, “Universal domain adaptation in fault diagnostics with hybrid weighted deep adversarial learning,” IEEE Trans. Industrial Informatics, vol. 17, no. 12, pp. 7957–7967, 2021.
[12]	C. Zhang, L im, A. K. Qin, and K. C. Tan, “Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics,” IEEE Trans. Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2306–2318, 2017. doi: 10.1109/TNNLS.2016.2582798
[13]	X. Li, Q. Ding, and J.-Q. Sun, “Remaining useful life estimation in prognostics using deep convolution neural networks,” Reliability Engineering &System Safety, vol. 172, pp. 1–11, 2018.
[14]	L. Guo, Y. Lei, N. Li, T. Yan, and N. Li, “Machinery health indicator construction based on convolutional neural networks considering trend burr,” Neurocomputing, vol. 292, pp. 142–150, 2018. doi: 10.1016/j.neucom.2018.02.083
[15]	W. Zhang and X. Li, “Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy,” IEEE/ASME Trans. Mechatronics, vol. 27, no. 1, pp. 430–439, 2022.
[16]	M. Yuan, Y. Wu, and L. Lin, “Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network,” in Proc. IEEE Int. Conf. Aircraft Utility Systems, pp. 135–140, 2016.
[17]	R. Zhao, J. Wang, R. Yan, and K. Mao, “Machine health monitoring with LSTM networks,” in Proc. 10th Int. Conf. Sensing Technology, pp. 1–6, 2016.
[18]	A. R.-T. Palazuelos, E. L. Droguett, and R. Pascual, “A novel deep capsule neural network for remaining useful life estimation,” Proc. the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 0, no. 0, p. 1748006X19866546, 2019.
[19]	P. da Costa, Y. Zhang, and U. Kaymak, “Remaining useful lifetime prediction via deep domain adaptation,” Reliability Engineering & System Safety, vol. 195, p. 106682, 2020.
[20]	H. Yan, K. Liu, X. Zhang, and J. Shi, “Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions,” IEEE Trans. Reliability, vol. 65, no. 3, pp. 1416–1426, 2016. doi: 10.1109/TR.2016.2575449
[21]	Mehta, A. Werner, and L. Mears, “Condition based maintenancesystems integration and intelligence using bayesian classification and sensor fusion,” Journal of Intelligent Manufacturing, vol. 26, no. 2, pp. 331–346, 2015. doi: 10.1007/s10845-013-0787-1
[22]	C. Song, K. Liu, and X. Zhang, “Integration of data-level fusion model and kernel methods for degradation modeling and prognostic analysis,” IEEE Trans. Reliability, vol. 67, no. 2, pp. 640–650, 2018. doi: 10.1109/TR.2017.2715180
[23]	X. Li, W. Zhang, H. Ma, Z. Luo, and X. Li, “Degradation alignment in remaining useful life prediction using deep cycle-consistent learning,” IEEE Trans. Neural Networks and Learning Systems, pp. 1–12, 2021.
[24]	L. Guo, Y. Lei, S. Xing, T. Yan, and N. Li, “Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data,” IEEE Trans. Industrial Electronics, vol. 66, no. 9, pp. 7316–7325, 2018.
[25]	S. Shao, Wa ng, and R. Yan, “Generative adversarial networks for data augmentation in machine fault diagnosis,” Computers in Industry, vol. 106, pp. 85–93, 2019. doi: 10.1016/j.compind.2019.01.001
[26]	A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. 26th Annual Conf. Neural Information Processing Systems, vol. 2, pp. 1097–1105, 2012.
[27]	G. S. Babu, P. Zhao, and X.-L. Li, “Deep convolutional neural network based regression approach for estimation of remaining useful life,” in Proc. Database Systems for Advanced Applications: 21st Int. Conf., pp. 214–228. Cham: Springer Int. Publishing, 2016.
[28]	G. Yarin and G. Zoubin, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in 33rd Int. Conf. Machine Learning, vol. 48, pp. 1050–1059, 2016.
[29]	P. Baldi and P. Sadowski, “Understanding dropout,” in 26th Int. Conf. Neural Information Processing Systems, vol. 2, p. 28142822, 2013.
[30]	I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative Adversarial Nets. Curran Associates, Inc., 2014.
[31]	Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” arXiv preprint arXiv: 1409.7495, 2015.
[32]	J. Zhang, Z. Ding, W. Li, and P. Ogunbona, “Importance weighted adversarial nets for partial domain adaptation,” arXiv preprint arXiv: 1803.09210, 2018.
[33]	J. Xu, Y. Wang, and L. Xu, “PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data,” IEEE Sensors Journal, vol. 14, no. 4, pp. 1124–1132, 2014. doi: 10.1109/JSEN.2013.2293517
[34]	A. Saxena and K. Goebel, “Turbofan engine degradation simulation data set,” NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA, 2008.
[35]	A. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation,” in Proc. Int. Conf. Prognostics and Health Management, pp. 1–9, 2008.
[36]	P. Nectoux, R. Gouriveau, K. Medjaher, E. Ramasso, B. Morello, N. Zerhouni, and C. Varnier, “PRONOSTIA: An experimental platform for bearings accelerated life test,” in Proc. IEEE Int. Conf. Prognostics and Health Management, Denver, CO, USA, 2012.
[37]	“IEEE PHM 2012 Data Challenge. [Online]. Available: http://www.femto-st.fr/kf/d/IEEEPHM2012-Challenge-Details.pdf.”
[38]	A. Soualhi, K. Medjaher, and N. Zerhouni, “Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression,” IEEE Trans. Instrumentation and Measurement, vol. 64, no. 1, pp. 52–62, 2015. doi: 10.1109/TIM.2014.2330494
[39]	F. O. Heimes, “Recurrent neural networks for remaining useful life estimation,” in Proc. Int. Conf. Prognostics and Health Management, pp. 1–6, 2008.
[40]	E. Ramasso, “Investigating computational geometry for failure prognostics,” Int. Journal of Prognostics and Health Management, vol. 5, no. 1, p. 005, 2014.
[41]	Z. Zhao, L. Bin, X. Wang, and W. Lu, “Remaining useful life prediction of aircraft engine based on degradation pattern learning,” Reliability Engineering &System Safety, vol. 164, pp. 74–83, 2017.
[42]	J. B. Coble and J. W. Hines, “Prognostic algorithm categorization with PHM challenge application,” in Proc. Int. Conf. Prognostics and Health Management, pp. 1–11, 2008.
[43]	P. F. Wang, B. D. Youn, and C. Hu, “A generic probabilistic framework for structural health prognostics and uncertainty management,” Mechanical Systems and Signal Processing, vol. 28, pp. 622–637, 2012. doi: 10.1016/j.ymssp.2011.10.019
[44]	J. Burriel-Valencia, R. Puche-Panadero, J. Martinez-Roman, A. SapenaBano, and M. Pineda-Sanchez, “Short-frequency Fourier transform for fault diagnosis of induction machines working in transient regime,” IEEE Trans. Instrumentation and Measurement, vol. 66, no. 3, pp. 432–440, 2017. doi: 10.1109/TIM.2016.2647458
[45]	K. K. Paliwal and L. D. Alsteris, “On the usefulness of STFT phase spectrum in human listening tests,” Speech Communication, vol. 45, no. 2, pp. 153–170, 2005. doi: 10.1016/j.specom.2004.08.001
[46]	C. Louen, S. X. Ding, and C. Kandler, “A new framework for remaining useful life estimation using support vector machine classifier,” in Proc. IEEE Conf. Control and Fault-Tolerant Systems, pp. 228–233, 2013.
[47]	S. Zheng, K. Ristovski, A. Farahat, and C. Gupta, “Long short-term memory network for remaining useful life estimation,” in Proc. IEEE Int. Conf. Prognostics and Health Management, pp. 88–95, 2017.
[48]	X. Li, W. Zhang, and Q. Ding, “A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning,” Neurocomputing, vol. 310, pp. 77–95, 2018. doi: 10.1016/j.neucom.2018.05.021
[49]	L. Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, pp. 2579–2625, 2008.

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(11) / Tables(8)

Get Citation

PDF

XML

Article Metrics

Article views (684) PDF downloads(114)

Highlights

An intelligent data-driven prognostic method is proposed for remaining useful life prediction
The challenging problem is addressed, where partial sensor malfunction occurs in the testing scenarios
Deep adversarial learning is proposed for extraction of generalized features from different sensors and entities
Experiments on popular prognosis datasets validate the effectiveness and superiority of the proposed method

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

doi: 10.1109/JAS.2022.105935

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content