Improving Identification of Wind Tunnel Systems Using Clustering-Based Ensemble Outlier Detection Model
Publication: Journal of Aerospace Engineering
Volume 34, Issue 3
Abstract
This paper proposes an outlier ensemble based on the clustering technique to improve the identification accuracy of wind tunnel systems, which are very critical pieces of test equipment in the field of aerospace. The use of clustering has two objectives in our outlier ensemble, that is, generating diverse training subsets and improving the robustness of base detectors. By analyzing the data characteristics of wind tunnel systems, we propose a hybrid criterion to determine the most appropriate clustering algorithm and the corresponding clustering number. This criterion is constituted by a qualitative and a quantitative criterion. The qualitative criterion is implemented first to eliminate several candidates from alternative clustering algorithms. Moreover, the quantitative criterion is used to determine the ultimate algorithm. In addition, a robust base detector is also developed with the assistance of the selected clustering algorithm. Finally, we verify the proposed detection model in two ways. In an offline application, the model is verified through two system identification models. In an online application, it is verified only through the performance of outlier detection.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all of the data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. All of the data used in this paper are sampled from a real-world transonic wind tunnel in an aerodynamic research institute in China. We have no right to release these datasets without the permission of this institute.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 51634002) and the National Key R & D Program of China (Grant No. 2017YFB0304104).
References
Aggarwal, C. C. 2017. Outlier analysis. 2nd ed. New York: Springer.
Bai, E.-W. 1998. “An optimal two-stage identification algorithm for Hammerstein–Wiener nonlinear systems.” Automatica 34 (3): 333–338. https://doi.org/10.1016/S0005-1098(97)00198-2.
Breiman, L. 1996. “Bagging predictors.” Mach. Learn. 24 (2): 123–140.
Breunig, M. M., H.-P. Kriegel, R. T. Ng, and J. Sander. 2000. “LOF: Identifying density-based local outliers.” In Proc., ACM Sigmod Record, 93–104. New York: Association for Computing Machinery. https://doi.org/10.1145/342009.335388.
Calinski, T., and J. Harabasz. 1974. “A dendrite method for cluster analysis.” Commun. Stat. Theory Methods 3 (1): 1–27. https://doi.org/10.1080/03610927408827101.
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.
Chandola, V., A. Banerjee, and V. Kumar. 2009. “Anomaly detection: A survey.” ACM Comput. Surv. 41 (3): 1–58. https://doi.org/10.1145/1541880.1541882.
Chiang, L. H., R. J. Pell, and M. B. Seasholtz. 2003. “Exploring process data with the use of robust outlier detection algorithms.” J. Process Control 13 (5): 437–449. https://doi.org/10.1016/S0959-1524(02)00068-9.
Cristianini, N., J. Shawe-Taylor, and J. S. Kandola. 2002. “Spectral kernel methods for clustering.” Adv. Neural Inf. Process. Syst. 14: 649–655.
Egan, W. J., S. L. Morgan, and A. Chem. 1998. “Outlier detection in multivariate analytical chemical data.” Anal. Chem. 70 (11): 2372. https://doi.org/10.1021/ac970763d.
Ester, M., H.-P. Kriegel, J. Sander, and X. Xu. 1996. “A density-based algorithm for discovering clusters in large spatial databases with noise.” In Proc., 2nd Int. Conf. on Knowledge Discovery and Data Mining, 226–231. Menlo Park, CA: Association for the Advancement of Artificial Intelligence.
Fukuda, E., S. Kokubo, J. Tanimoto, W. Zhen, A. Hagishima, and N. Ikegaya. 2014. “Risk assessment for infectious disease and its impact on voluntary vaccination behavior in social networks.” Chaos, Solitons Fractals 68 (68): 1–9.
He, X., D. Cai, Y. Shao, H. Bao, and J. Han. 2011. “Laplacian regularized gaussian mixture model for data clustering.” IEEE Trans. Knowl. Data Eng. 23 (9): 1406–1418. https://doi.org/10.1109/TKDE.2010.259.
Ho, T. K. 1998. “The random subspace method for constructing decision forests.” IEEE Trans. Pattern Anal. Mach. Intell. 20 (8): 832–844. https://doi.org/10.1109/34.709601.
Jeong, I. K., and J.-J. Lee. 1996. “Adaptive simulated annealing genetic algorithm for system identification.” Eng. Appl. Artif. Intell. 9 (5): 523–532. https://doi.org/10.1016/0952-1976(96)00049-8.
Jin, Q., Z. Wang, Z. Wang, and Y. L. Wang. 2006. “Strategy changing penalty promotes cooperation in spatial prisoner’s dilemma game.” Chaos, Solitons Fractals 28 (4): 395–401.
Kittler, J., M. Hatef, R. P. W. Duin, and J. Matas. 1998. “On combining classifiers.” IEEE Transactions on Pattern Analysis & Machine Intelligence 20 (3): 226–239. https://doi.org/10.1109/34.667881.
Krawczyk, B., M. Woźniak, and B. Cyganek. 2014. “Clustering-based ensembles for one-class classification.” Inf. Sci. 264 (6): 182–195. https://doi.org/10.1016/j.ins.2013.12.019.
Liu, F. T., K. M. Ting, and Z.-H. Zhou. 2012. “Isolation-based anomaly detection.” ACM Trans. Knowl. Discovery Data 6 (1): 3.
Luitel, B., and G. K. Venayagamoorthy. 2010. “Particle swarm optimization with quantum infusion for system identification.” Eng. Appl. Artif. Intell. 23 (5): 635–649. https://doi.org/10.1016/j.engappai.2010.01.022.
Maulik, U., and S. Bandyopadhyay. 2002. “Performance evaluation of some clustering algorithms and validity indices.” IEEE Trans. Pattern Anal. Mach. Intell. 24 (12): 1650–1654. https://doi.org/10.1109/TPAMI.2002.1114856.
Nowak, R. 2003. “Distributed EM algorithms for density estimation and clustering in sensor networks.” IEEE Trans. Signal Process. 51 (8): 2245–2253. https://doi.org/10.1109/TSP.2003.814623.
Soeterboek, R. A. M., A. F. Pels, H. B. Verbruggen, and G. C. A. Van Langen. 1991. “A predictive controller for the Mach number in a transonic wind tunnel.” IEEE Control Syst. 11 (1): 63–72. https://doi.org/10.1109/37.103359.
Swersky, L., H. O. Marques, J. Sander, R. J. G. B. Campello, and A. Zimek. 2016. “On the evaluation of outlier detection and one-class classification methods.” In Proc., IEEE Int. Conf. on Data Science and Advanced Analytics. New York: IEEE.
Tax, D. M. J. 2001. “One-class classification (concept-learning in the absence of counter-examples).” Ph.D. thesis, Delft Univ. of Technology. https://elibrary.ru/item.asp?id=5230402.
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.
Van Overschee, P., and B. De Moor. 1994. “N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems.” Automatica 30 (1): 75–93. https://doi.org/10.1016/0005-1098(94)90230-5.
Verma, D., and M. Meila. 2003. A comparison of spectral clustering algorithms. Seattle, WA: Dept. of CSE, Univ. of Washington Seattle.
Walczak, B., and D. L. Massart. 1995. “Robust principal components regression as a detection tool for outliers.” Chemom. Intell. Lab. Syst. 27 (1): 41–54. https://doi.org/10.1016/0169-7439(95)80006-U.
Wang, B., Z. Mao, and K. Huang. 2017. “Detecting outliers in complex nonlinear systems controlled by predictive control strategy.” Chaos, Solitons Fractals 103 (Oct): 588–595. https://doi.org/10.1016/j.chaos.2017.07.018.
Wang, D., P. W. Tse, W. Guo, and Q. Miao. 2011. “Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis.” Meas. Sci. Technol. 22 (2): 025102. https://doi.org/10.1088/0957-0233/22/2/025102.
Wang, X., S. Li, W. Cai, H. Yue, X. Zhou, and T. Chai. 2005. “Multi-model direct adaptive decoupling control with application to the wind tunnel system.” ISA Trans. 44 (1): 131–143. https://doi.org/10.1016/S0019-0578(07)60050-0.
Wang, X., P. Yuan, and Z. Mao. 2015. “Ensemble fixed-size LS-SVMs applied for the Mach number prediction in transonic wind tunnel.” IEEE Trans. Aerosp. Electron. Syst. 51 (4): 3167–3181. https://doi.org/10.1109/TAES.2014.130708.
Wang, X., P. Yuan, and Z. Mao. 2016. “The modified feature subsets ensemble applied for the Mach number prediction in wind tunnel.” IEEE Trans. Aerosp. Electron. Syst. 52 (2): 863–874. https://doi.org/10.1109/TAES.2015.150100.
Wu, X., V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, and S. Y. Philip. 2008. “Top 10 algorithms in data mining.” Knowl. Inf. Syst. 14 (1): 1–37. https://doi.org/10.1007/s10115-007-0114-2.
Yu, F., Z. Mao, P. Yuan, D. He, and M. Jia. 2017. “Recursive parameter estimation for Hammerstein-Wiener systems using modified EKF algorithm.” ISA Trans. 70 (Sep): 104–115. https://doi.org/10.1016/j.isatra.2017.05.012.
Zhang, T., R. Ramakrishnan, and M. Livny. 1997. “BIRCH: A new data clustering algorithm and its applications.” Data Min. Knowl. Discovery 1 (2): 141–182. https://doi.org/10.1023/A:1009783824328.
Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 34 • Issue 3 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Dec 18, 2019
Accepted: Sep 16, 2020
Published online: Feb 13, 2021
Published in print: May 1, 2021
Discussion open until: Jul 13, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.