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