A Turboshaft Aeroengine Fault Detection Method Based on One-Class Support Vector Machine and Transfer Learning
Publication: Journal of Aerospace Engineering
Volume 35, Issue 6
Abstract
The fault detection of turboshaft engines is very important to ensure the flight safety of helicopters. Because there are few fault data in engine historical operation data, engine fault detection is often regarded as an anomaly detection problem, which is solved by the one-class classification (OCC) method. However, previous studies on fault detection usually ignored the difference between engine data caused by different engine states and operating conditions. Therefore, this paper considers the existence of these differences and introduces transfer learning to solve the problem. In this paper, based on one-class support vector machines (OC-SVM) and transfer learning (TL), an algorithm named OC-SVM-TL is proposed to detect turboshaft engine faults. In this algorithm, the hyperplane of the OC-SVM is transferred from the source domain to the target domain as a knowledge structure to help the target domain to establish a fault detection model with high accuracy. The training process is divided into two steps. The first step is to train the OC-SVM model with the data of the source domain, and the second step is to train the OC-SVM model with the data of the target domain, and the hyperplane difference between the source domain and the target domain is considered in the training process. Finally, the fault detection experiment of turboshaft engine was designed, and the fault detection of turboshaft engine was carried out under different working conditions and different engine states. The experimental results showed that the proposed algorithm has good fault detection performance when the target domain data are few or the amount of target domain data changes.
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Data Availability Statement
Some, models, or codes generated or used during the study are proprietary, or confidential in nature and may only be provided with restrictions. The turboshaft aeroengine model cannot be provided because it is proprietary, and it is a part of an ongoing project. Some algorithm codes can be provided upon reasonable requests.
Acknowledgments
This research was supported by the Fundamental Research Funds for the Central Universities under Grant No. NS2022027.
References
Bai, Y., Y. J. Li, B. W. Zhang, and Y. C. Zhao. 2020. “Intelligent fault diagnosis of aeroengine based on algorithm fusion.” In Proc., 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conf., 255–261. New York: IEEE.
Ben Khediri, I., C. Weihs, and M. Limam. 2012. “Kernel k-means clustering based local support vector domain description fault detection of multimodal processes.” Expert Syst. Appl. 39 (2): 2166–2171. https://doi.org/10.1016/j.eswa.2011.07.045.
Borgwardt, K. M., A. Gretton, M. J. Rasch, H. P. Kriegel, B. Schölkopf, and A. J. Smola. 2006. “Integrating structured biological data by kernel maximum mean discrepancy.” Bioinformatics 22 (14): e49–e57. https://doi.org/10.1093/bioinformatics/btl242.
Camerini, V., G. Coppotelli, and S. Bendisch. 2018. “Fault detection in operating helicopter drivetrain components based on support vector data description.” Aerosp. Sci. Technol. 73 (Feb): 48–60. https://doi.org/10.1016/j.ast.2017.11.043.
Cha, M., J. S. Kim, and J. G. Baek. 2014. “Density weighted support vector data description.” Expert Syst. Appl. 41 (7): 3343–3350. https://doi.org/10.1016/j.eswa.2013.11.025.
Cui, J. G., Y. Tian, X. Cui, J. L. Wang, L. Y. Jiang, and M. Y. Yu. 2020. “A method for fault diagnosis of aviation engine gas system.” In Proc., 39th Chinese Control Conf., 4244–4248. New York: IEEE.
Ganin, Y., E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky. 2016. “Domain-adversarial training of neural networks.” J. Mach. Learn. Res. 17: 59.
Leng, Q., H. Qi, J. Miao, W. Zhu, and G. Su. 2015. “One-class classification with extreme learning machine.” Math. Probl. Eng. 2015 (May): 412957. https://doi.org/10.1155/2015/412957.
Li, B., Y.-P. Zhao, and Y.-B. Chen. 2021. “Unilateral alignment transfer neural network for fault diagnosis of aircraft engine.” Aerosp. Sci. Technol. 118 (Nov): 107031. https://doi.org/10.1016/j.ast.2021.107031.
Long, M. S., Y. Cao, J. M. Wang, and M. I. Jordan. 2015. “Learning transferable features with deep adaptation networks.” In Vol. 37 of Proc., Int. Conf. on Machine Learning, 97–105. New York: International Machine Learning Society.
Long, M. S., J. M. Wang, G. G. Ding, J. G. Sun, and P. S. Yu. 2013. “Transfer feature learning with joint distribution adaptation.” In Proc., 2013 IEEE Int. Conf. on Computer Vision, 2200–2207. New York: IEEE. https://doi.org/10.1109/ICCV.2013.274.
Lu, F., T. B. Zhu, and Y. Q. Lv. 2013. “Data-driven based gas path fault diagnosis for turbo-shaft engine.” Appl. Mech. Mater. 249–250 (Dec): 400–404. https://doi.org/10.4028/www.scientific.net/AMM.249-250.400.
Pan, S. J., I. W. Tsang, J. T. Kwok, and Q. A. Yang. 2011. “Domain adaptation via transfer component analysis.” IEEE Trans. Neural Networks 22 (2): 199–210. https://doi.org/10.1109/TNN.2010.2091281.
Pan, S. J., and Q. A. Yang. 2010. “A survey on transfer learning.” IEEE Trans. Knowl. Data Eng. 22 (10): 1345–1359. https://doi.org/10.1109/TKDE.2009.191.
Schölkopf, B., R. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt. 2000. “Support vector method for novelty detection.” Adv. Neural Inf. Process. Syst. 12: 582–588.
Tax, D. M. J., and R. P. W. Duin. 2004. “Support vector data description.” Mach. Learn. 54 (1): 45–66. https://doi.org/10.1023/B:MACH.0000008084.60811.49.
Tayarani-Bathaie, S. S., and K. Khorasani. 2016. “Fault detection and isolation of gas turbine engines using a bank of neural networks.” J. Process Control 36 (Dec): 22–41. https://doi.org/10.1016/j.jprocont.2015.08.007.
Wang, Z. F., J. L. Zarader, and S. Argentieri. 2012. “A novel aircraft engine fault diagnostic and prognostic system based on SVM.” In Proc., 2012 IEEE Int. Conf. on Condition Monitoring and Diagnosis, 723–728. New York: IEEE.
Xie, J. Y., L. B. Zhang, L. X. Duan, and J. J. Wang. 2016. “On cross-domain feature fusion in gearbox fault diagnosis under various operating conditions based on transfer component analysis.” In Proc., 2016 IEEE Int. Conf. on Prognostics and Health Management. New York: IEEE. https://doi.org/10.1109/ICPHM.2016.7542845.
Yan, W. Z. 2016. “One-class extreme learning machines for gas turbine combustor anomaly detection.” In Proc., 2016 Int. Joint Conf. on Neural Networks, 2909–2914. New York: IEEE.
Yang, J. H., T. Q. Deng, and R. Sui. 2016. “An adaptive weighted one-class SVM for robust outlier detection.” In Vol. 359 of Proc., 2015 Chinese Intelligent Systems Conf., 475–484. Berlin: Springer Verlag. https://doi.org/10.1007/978-3-662-48386-2_49.
Yin, S., X. P. Zhu, and C. Jing. 2014. “Fault detection based on a robust one class support vector machine.” Neurocomputing 145 (Dec): 263–268. https://doi.org/10.1016/j.neucom.2014.05.035.
Zhao, L., C. Y. Mo, T. T. Sun, W. Huang, and X. J. Wan. 2020a. “Aero engine gas-path fault diagnose based on multimodal deep neural networks.” Wireless Commun. Mobile Comput. 2020 (Oct): 8891595. https://doi.org/10.1155/2020/8891595.
Zhao, Y.-P., G. Huang, Q.-K. Hu, and B. Li. 2020b. “An improved weighted one class support vector machine for turboshaft engine fault detection.” Eng. Appl. Artif. Intell. 94 (Sep): 103796. https://doi.org/10.1016/j.engappai.2020.103796.
Zhao, Y.-P., G. Huang, Q.-K. Hu, J.-F. Tan, J.-J. Wang, and Z. Yang. 2019. “Soft extreme learning machine for fault detection of aircraft engine.” Aerosp. Sci. Technol. 91 (Aug): 70–81. https://doi.org/10.1016/j.ast.2019.05.021.
Zhao, Y.-P., F.-Q. Song, Y.-T. Pan, and B. Li. 2018. “Retargeting extreme learning machines for classification and their applications to fault diagnosis of aircraft engine.” Aerosp. Sci. Technol. 71 (Dec): 603–618. https://doi.org/10.1016/j.ast.2017.10.004.
Zhao, Y.-P., Y.-L. Xie, and Z.-F. Ye. 2021. “A new dynamic radius SVDD for fault detection of aircraft engine.” Eng. Appl. Artif. Intell. 100 (Apr): 104177. https://doi.org/10.1016/j.engappai.2021.104177.
Zhong, S. S., S. Fu, and L. Lin. 2019. “A novel gas turbine fault diagnosis method based on transfer learning with CNN.” Measurement 137 (Apr): 435–453. https://doi.org/10.1016/j.measurement.2019.01.022.
Zhou, Y. M., K. Wu, Z. J. Meng, and M. J. Tian. 2017. “Fault detection of aircraft based on support vector domain description.” Comput. Electr. Eng. 61 (Jul): 80–94. https://doi.org/10.1016/j.compeleceng.2017.06.016.
Zhu, F., J. Yang, C. Gao, S. Xu, N. Ye, and T. M. Yin. 2016. “A weighted one-class support vector machine.” Neurocomputing 189 (May): 1–10. https://doi.org/10.1016/j.neucom.2015.10.097.
Zhuang, F. Z., Z. Y. Qi, K. Y. Duan, D. B. Xi, Y. C. Zhu, H. H. Zhu, H. Xiong, and Q. He. 2021. “A comprehensive survey on transfer learning.” Proc. IEEE 109 (1): 43–76. https://doi.org/10.1109/JPROC.2020.3004555.
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Published In
Journal of Aerospace Engineering
Volume 35 • Issue 6 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jan 15, 2022
Accepted: Jun 8, 2022
Published online: Aug 3, 2022
Published in print: Nov 1, 2022
Discussion open until: Jan 3, 2023
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