Dataset Generation Based on 1D-CAE Modeling for Fault Diagnostics in a Spacecraft Propulsion System

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Published Sep 4, 2023
Kohji Tominaga Yu Daimon Masao Toyama Kazushi Adachi Seiji Tsutsumi Noriyasu Omata Taiichi Nagata

Abstract

The objective of this study is to generate a classified dataset of valve faults and bubble contamination anomalies in the propellant supply pipe of spacecraft propulsion systems. The dataset is available in PHMAP23, and the paper intends to describe its characteristics. The dataset includes time and pressure information and has been generated through numerical simulations using SimlationX, a 1D-CAE software. The condition of the propulsion system is reflected by the characteristics of the pressure dynamic response generated by the water hammer in the supply pipe caused by the rapid opening and closing of the downstream solenoid valve. Therefore, accurate classification of anomalies and faults can be achieved by extracting characteristics from the pressure dynamic response waveform.

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Keywords

Anomaly detection, PHM, SimulationX, Data generation

References
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K. Adachi, S. Khan, K. Tominaga, N. Omata, S. Tsutsumi, and T. Nagata, (2023). Study on Anomaly Detection of a Spacecraft Propulsion System using Machine Learning. Asian Joint Conference on Propulsion and Power 2023, March 15-18, Ishikawa, Japan.

Y. Daimon; K. Tominaga; G. Fujii; T. Nagata; Y. Matsuura; Y. Kano; E. Uchiyama, (2023). One-dimensional Modeling of Ignition Timing for Hypergolic Bipropellant Thrusters. Aerospace Europe Conference 2023. July 9-13, Lausanne, Switzerland. SimulationX. https://www.simulationx.com/
Section
Data Challenge Papers