Improvement in Identification Accuracy of a Failure Diagnostic System for a Reusable Rocket Engine

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Published Sep 4, 2023
Fumihisa Nagashima Hatsuo Mori Yasuhiro Ishikawa Sato Masaki Tomoyuki Hashimoto

Abstract

As a technology for safe and efficient operation of reusable rockets, we are developing failure diagnosis technology for reusable rocket engines. In order to follow the changes in rocket engine operating conditions, a failure diagnostic method which monitors an error vector: the difference between the predicted and measured values of the sensors was developed. The method contains anomaly detection by Mahalanobis distance and failure identification by support vector machines (SVMs). In this report, combinations of monitoring sensors of SVMs for each failure mode were optimized by using design of experiments. By using optimal sensor combinations, the F-score of SVMs were improved in all failure modes. From the results of the orthogonal table experiments, it was supposed that sensors which show the difference in failure modes are important to distinguish failure modes. In addition, a failure classifier combined with the optimized SVMs for each failure mode was developed and demonstrated. The performance of the combined failure classifier with the optimal sensor combination was mostly greater than that with all sensors. However, degradation of the classification performance was also obtained. It is necessary to consider how integrate the results of SVMs which are optimized individually.

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Keywords

Health monitoring, Failure diagnostic, Reusable rocket engine, Support vector machine, Error vector, Sensor optimization

References
Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp.2623-2631), August 4-8, Anchorage AK, USA. doi: 10.1145/3292500.3330701

Maru, Y., Mori, H., Ogai, T., Mizukoshi, N., Takeuchi, S., Yamamoto, T., Yagishita, T., & Nonaka, S. (2018). Anomaly detection configured as a combination of state observer and Mahalanobis-Taguchi method for a rocket engine. Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, vol. 16 (2), pp. 195-201. doi: 10.2322/tastj.16.195

Nagashima, F., Hashizume, T., Mori, H., Ishikawa, Y., & Hashimoto, T., (2022). Development of failure diagnostic system for a reusable rocket engine using simulation. SICE Annual Conference 2022 (ThB07.3), September 6-9, Kumamoto, Japan.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, vol. 12, pp. 2825-2830.

Ukai, S., Sakaki, S., Ishikawa, Y., Sakaguchi, H., & Ishihara, S. (2019). Component tests of a LOX/methane fullexpander cycle rocket engine: Injector and regeneratively cooled combustion chamber. 8th European Conference for Aeronautics and Space Sciences. July 1-4, Madrid, Spain
Section
Special Session Papers