Anomaly Detection in Spacecraft Propulsion System using Time Series Classification based on K-NN

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Published Sep 4, 2023
Yoshiki Kato Taku Kato Tsubasa Tanaka

Abstract

In this paper, we propose an anomaly detection method developed by the team called “Team Tsubasa” in the PHMAP2023 Data Challenge. This is an anomaly detection competition for spacecraft propulsion systems (PHM Society, 2023). We joined the Data Challenge with the aim of deepening our knowledge of anomaly detection technology through the competition. In spacecraft propulsion systems, solenoid valve faults and bubble anomalies can occur, and it is considered important to detect them. Also, when other unknown anomalies occur, it is necessary to detect them without confusing them with known anomalies. In this paper, we propose time series classification by k-NN algorithm (Cover & Hart, 1967) as one of the methods to detect these anomalies. In this data challenge, we tried to classify anomalies by k-NN and to identify the location of the anomalies. For those classified as solenoid valve faults, we estimated the opening ratio of the solenoid valve from the similarity of the time series waveforms. As a result, the proposed method achieved a score of 99.05% based on the scoring rules given by the PHMAP 2023 Secretariat and our team won third place.

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Keywords

KNN, Fault detection, Classification

References
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, vol.13, pp. 21-27.

Nakamura, T., Imamura, M., Mercer, R., & Keogh, E. (2020). Merlin: Parameter-free discovery of arbitrary length anomalies in massive time series archives. 2020 IEEE international conference on data mining (ICDM). IEEE

Nakamura, T., Mercer, R., Imamura, M., & Keogh, E. (2023). MERLIN++: parameter-free discovery of time series anomalies. Data Mining and Knowledge Discovery, vol. 37, pp. 670-709.

PHM Society. (2023). PHM Conference Data Challenge. Retrieved 18 July 2023 from https://data.phmsociety.org/phmap-2023-data-challenge

Tominaga, K., Daimon, Y., Toyama, M., Adachi, K., Tsutsumi, S., Omata, N., & Nagata, T. (2023). Dataset generation based on 1D-CAE modeling for fault diagnostics in a spacecraft propulsion system. Asia Pacific Conference of the Prognostics and Health Management Society 2023 (PHMAP 2023)
Section
Data Challenge Papers