Demonstration of model-based real-time anomaly detection in a JAXA 6.5m×5.5m low-speed wind tunnel.

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Published Sep 4, 2023
Shotaro Hamato Seiji Tsutsumi Hirotaka Yamashita Tatsuro Shiohara Tomonari Hirotani Hiroyuki Kato

Abstract

In this study, real-time anomaly detection in a wind tunnel was conducted using a threshold based on uncertainty quantification of a numerical model. A model-based numerical model of a wind tunnel was developed, and the uncertainty consisting of input uncertainty, model form uncertainty, and numerical approximation was quantitatively evaluated. The threshold of anomaly obtained here was demonstrated in a 6.5m×5.5m wind tunnel of Japan Aerospace Exploration Agency (JAXA). Synthetic anomaly injected into the measurement system was successfully detected.

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Keywords

Real-time anomaly detection, Wind tunnel, Uncertainty quantification

References
Hamato, S., Tsutsumi, S., Yamashita, H., Shiohara, T., Hirotani, T., & Kato, H. (2022). Development of digital twin for real-time anomaly detection in JAXA’s 6.5m×5.5m low-speed wind tunnel. Proceedings of 54th Fluid Dynamics Conference and 40th Aerospace Numerical Simulation Symposium. June 29-July 1. Morioka, Japan.

Idelʹchik, I. E. (1996). Handbook of Hydraulic Resistance. Begell House.

Kandukuri, S. T., Klausen, A., Karimi, H. R., & Robbersmyr, K. G. (2016). A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable and Sustainable Energy Reviews, vol. 53, pp. 697–708. doi:10.1016/j.rser.2015.08.061.

Koga, S., Kohzai, M., Ueno, M., Nakakita, K., & Sudani, N. (2013). Analysis of NASA Common Research Model Dynamic Data in JAXA Wind Tunnel Tests. Proceedings of 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, in Aerospace Sciences Meetings. American Institute of Aeronautics and Astronautics. July 7-10. doi: 10.2514/6.2013-495.

Levy, D. W., Laflin, K. R., Tinoco, E. N., Vassberg, J. C., Mani, M., Rider, B., Rumsey, C. L., Wahls, R. A., Morrison, J. H., Brodersen, O. P., Crippa, S., Mavriplis, D. J., & Murayama, M. (2014). Summary of Data from the Fifth Computational Fluid Dynamics Drag Prediction Workshop. Journal of Aircraft, vol.51, pp.1194–1213. doi: 10.2514/1.C032389.

Ogata, H. (2009). Uncertainty in risk analysis. Japanese Journal of Risk Analysis, vol. 19(2), pp. 3–9.

Pecht, M., & Kang, M. (2018). Prognostics and health management of electronics: fundamentals, machine learning, and internet of things (2nd ed.). Hoboken: Wiley-IEEE press.

Roy, C. J., & Oberkampf, W. L. (2011). A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing. Computer Methods in Applied Mechanics and Engineering, vol.200(25-28), pp. 2131–2144. doi:10.1016/j.cma.2011.03.016.

Shigemi, T., & Hirooka, K. (1967). 6-m. Low-Speed Wind Tunnel at the National Aerospace Laboratory. The Journal of the Japan Society of Aeronautical Engineering, vol. 15(167), pp. 408–417. doi: 10.2322/jjsass1953.15.408.

Tahan, M., Tsoutsanis, E., Muhammad, M., & Abdul Karim, Z. A. (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy, vol. 198, pp. 122–144. doi: 10.1016/j.apenergy.2017.04.048.

Voyles, I. T., & Roy, C. J. (2015). Evaluation of Model Validation Techniques in the Presence of Aleatory and Epistemic Input Uncertainties. Proceedings of 17th AIAA Non-Deterministic Approaches Conference. Kissimmee. January 5-9. Florida. doi: 10.2514/6.2015-1374.

Wang, D., Tsui, K.-L., & Miao, Q. (2018). Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators. IEEE Access, vol. 6, pp. 665–676. doi: 10.1109/ACCESS.2017.2774261.
Section
Special Session Papers