A Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems
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Published
Oct 8, 2024
Kazushi ADACHI
Samir Khan
Shinichi NAKASUKA
Kohji TOMINAGA
Seiji Tsutsumi
Yu DAIMON
Taiichi NAGATA
Abstract
The increasing complexity of deep space missions introduces significant challenges in maintaining spacecraft health, particularly in the propulsion systems, due to the inherent communication delays with Earth. This research proposes a novel framework for the autonomous, data-driven fault diagnosis of spacecraft propulsion systems. Leveraging data generated from a spacecraft propulsion system simulation model. The study addresses the limitations imposed by computational resources and sensor installation constraints through Sequential Forward Selection (SFS) for optimized sensor placement and feature selection. The framework's effectiveness is demonstrated through implementation on a microcomputer, showing promising results in terms of diagnostic accuracy and processing speed, thus highlighting its potential for onboard spacecraft application. This study not only advances the autonomous capabilities of spacecraft in deep space but also contributes to the broader field of Prognostics and Health Management (PHM) by providing a scalable, efficient approach to fault diagnosis in critical spacecraft systems. The proposed framework demonstrates a promising approach to optimizing diagnostic tasks for spacecraft systems. However, the trade-offs observed necessitate a careful consideration of task-specific requirements and the potential need for adjustments to maintain a high level of accuracy alongside computational efficiency.
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Keywords
Anomaly detection, propulsion systems, data-driven, Sequential Forward Selection, autonomy
References
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C. Caini, R. Firrincieli, T. de Cola, I. Bisio, M. Cello, and G. Acar, “Mars to Earth communications through orbiters: Delay Tolerant/Disruption‒Tolerant Networking performance analysis”, International Journal of Satellite Communications and Networking, vol. 32, no. 2, pp. 127–140, 2014.
S. Khan and T. Yairi, “A review on the application of deep learning in system health management”, Mechanical Systems and Signal Processing, vol. 107, pp. 241–265, 2018.
K. O. Kolcio, “Model‒based fault detection and isolation system for increased autonomy”, AIAA SPACE 2016. p. 5225, 2016.
K. Kolcio and L. Fesq, “Model‒based off‒nominal state isolation and detection system for autonomous fault management”, in 2016 IEEE Aerospace Conference, 2016, pp. 1–13. doi:10.1109/AERO.2016.7500793.
K. Kolcio, L. Fesq, and R. Mackey, “Model‒based approach to rover health assessment for increased productivity”, in 2017 IEEE Aerospace Conference, 2017, pp. 1–13. doi: 10.1109/AERO.2017.7943835.
A. Nikora, P. Srivastava, L. Fesq, S. Chung, and K. Kolcio, “Assurance of model‒based fault diagnosis”, in 2018 IEEE Aerospace Conference, 2018, pp. 1–14. doi: 10.1109/AERO. 2018.8396550.
G. B. Aaseng, “Scaling up model‒based diagnostic and fault effects reasoning for spacecraft”, in AIAA SPACE 2015 Conference and Exposition, 2015, p. 4465.
D. Henry, A. A. Barahona, N. Ellero, V. Dubanchet, P. Colmenarejo, and F. Ankersen, “Model‒based fault diagnosis and fault‒tolerant data fusion algorithms for the ESA’s In‒Orbit‒Assembly project”, in 2021 5th International Conference on Control and Fault‒Tolerant Systems (SysTol), 2021, pp. 211–218. doi: 10.1109/SysTol52990.2021.9595822.
D. Henry, C. Le Peuvédic, L. Strippoli, and F. Ankersen, “Model‒based FDIR and Fault Accommodation for a Ren‒ dezvous Mission around the Mars Planet: the Mars Sample Return Case”, IFAC‒PapersOnLine, vol. 49, no. 5, pp. 266– 271, 2016, doi: https://doi.org/10.1016/j.ifacol.2016.07.124.
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K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding”, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD '18. London, United Kingdom: Association for Computing Machinery, 2018, p. 387. doi: 10.1145/3219819.3219845.
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M. Yamamoto et al., “Surge Pressure Management in HTV Propulsion System”, in 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 2007.
B. Brindle, J. Gilbert, J. Moore, and G. Risha, “Effects of Water Hammer on Propulsion Systems”, 2016, p. [24] E. B. Wylie, V. L. Streeter, and L. Suo, Fluid transients in sys‒ tems, vol. 1. Prentice Hall Englewood Cliffs, NJ, 1993.
A. Kodura, “An analysis of the impact of valve closure time on the course of water hammer”, Archives of Hydro‒Engineering and Environmental Mechanics, vol. 63, no. 1, 2016.
A. Kumar, P. S. Prasad, and M. R. Rao, “Experimental studies of water hammer in propellant feed system of reaction control system”, Propulsion and Power Research, vol. 7, no. 1, pp. 52–59, 2018.
R. Lecourt and J. Steelant, “Experimental investigation of wa‒ terhammer in simplified feed lines of satellite propulsion systems”, Journal of Propulsion and Power, vol. 23, no. 6, pp. 1214–1224, 2007.
K. Kawatsu et al., “Resilient redundant spacecraft GN&C sys‒ tem fault detection and diagnostics”, in Aerospace Europe Conference, AEC2, 2020.
K. Kawatsu, C. Inoue, Y. Terauchi, Y. Daimon, G. Fujii, and A. Noumi, “System‒level Integrated Modeling and Simulation Targeting Space Propulsion System for Design Evaluation and Risk Assessment”, in 32nd International Symposium on Space Technology and Science, 2019.
G. Fujii et al., “Verification of the Transient Behavior of the SLIM Propulsion System (Smart Lander for Investigating Moon)”, in AIAA Propulsion and Energy 2021 Forum, 2021, p. 3582.
H. YAMAMOTO, K. KAWATSU, Y. DAIMON, and G. FUJII, “Development of a Dynamic Response Model Considering Trapped Gas Effect for Spacecraft Liquid Propulsion Systems”, Journal of Evolving Space Activities, vol. 1, no., p. 34, 2023, doi: 10.57350/jesa.34.
K. KAWATSU, Y.‒i. IZATO, and A. MIYAKE, “Framework for Model‒Based Approach to Life Cycle Risk Management for Spacecraft Liquid Propulsion System”, Journal of Evolving Space Activities, vol. 1, p. 78, 2023.
K. Kawatsu, G. Fujii, T. Nagata, H. Yamamoto, and Y. Dai‒ mon, “Preliminary Demonstration of Concept for Fault Diagnosis in Resilient Redundant Spacecraft Propulsion”, in AIAA SCITECH 2023 Forum, 2023, p. 1259.
K. Tominaga et al., “Dataset Generation Based on 1D‒CAE Modeling for Fault Diagnostics in a Spacecraft Propulsion System”, in PHM Society Asia‒Pacific Conference, 2023.
T. Minami and J. Lee, “PHM for Spacecraft Propulsion Sys‒ tems: Similarity‒Based Model and Physics‒Inspired Features”, in PHM Society Asia‒Pacific Conference, 2023.
S. K. Lee et al., “Hybrid Approach of XGBoost and Rule‒based Model for Fault Detection and Severity Estimation in Spacecraft Propulsion System”, in PHM Society Asia‒Pacific Conference, 2023.
Y. Kato, T. Kato, and T. Tanaka, “Anomaly Detection in Space‒ craft Propulsion System using Time Series Classification based on K‒NN”, in PHM Society Asia‒Pacific Conference, 2023.
B. Schölkopf, J. C. Platt, J. Shawe‒Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high‒dimensional distribution”, Neural computation, vol. 13, no. 7, pp. 1443– 1471, 2001.
E. Fix and J. L. Hodges, “Discriminatory analysis. Nonparametric discrimination: Consistency properties”, International Statistical Review/Revue Internationale de Statistique, vol. 57, no. 3, pp. 238–247, 1989.
S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient algorithms for mining outliers from large data sets”, in Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, pp. 427–438.
P. C. Mahalanobis, “On the generalized distance in statistics”, Sankhyā: The Indian Journal of Statistics, Series A (2008‒), vol. 80, p. S1‒‒S7, 2018.
C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning, vol. 4, no. 4. Springer, 2006.
B. Awwad Shiekh Hasan and J. Q. Gan, “Sequential EM for unsupervised adaptive Gaussian mixture model based classifier”, in International Workshop on Machine Learning and Data Mining in Pattern Recognition, 2009, pp. 96–106.
P. Karsmakers, K. Pelckmans, and J. A. K. Suykens, “Multi‒ class kernel logistic regression: a fixed‒size implementation”, in 2007 International Joint Conference on Neural Networks, 2007, pp. 1756–1761. doi: 10.1109/IJCNN.2007.4371223.
A. Widodo and B.‒S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis”, Mechanical systems and signal processing, vol. 21, no. 6, pp. 2560–2574, 2007.
J. Platt, “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods”, Adv. Large Margin Classif., vol. 10, p., 2000.
I. Guyon and A. Elisseeff, “An introduction to variable and feature selection”, Journal of machine learning research, vol. 3, no. Mar, pp. 1157–1182, 2003.
S. Bouatmane, M. A. Roula, A. Bouridane, and S. Al‒Maadeed, “Round‒Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispec‒ tral imagery”, Machine Vision and Applications, vol. 22, pp. 865–878, 2011.
S. Shafiee, L. M. Lied, I. Burud, J. A. Dieseth, M. Alsheikh, and M. Lillemo, “Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery”, Computers and Electronics in Agriculture, vol. 183, p. 106036, 2021, doi: https://doi.org/10.1016/j.compag.2021.106036.
Raspberry Pi Foundation, “Raspberry Pi Zero W”. 2023.
J. Danos, C. Page, S. Jones, and B. Lewis, “USU’s GASPACS CubeSat”, 2022.
M. K. Biswal M and R. N. Annavarapu, “Human Mars Exploration and Expedition Challenges”, in AIAA Scitech 2021 Forum, 2021, p. 628.
C. Caini, R. Firrincieli, T. de Cola, I. Bisio, M. Cello, and G. Acar, “Mars to Earth communications through orbiters: Delay Tolerant/Disruption‒Tolerant Networking performance analysis”, International Journal of Satellite Communications and Networking, vol. 32, no. 2, pp. 127–140, 2014.
S. Khan and T. Yairi, “A review on the application of deep learning in system health management”, Mechanical Systems and Signal Processing, vol. 107, pp. 241–265, 2018.
K. O. Kolcio, “Model‒based fault detection and isolation system for increased autonomy”, AIAA SPACE 2016. p. 5225, 2016.
K. Kolcio and L. Fesq, “Model‒based off‒nominal state isolation and detection system for autonomous fault management”, in 2016 IEEE Aerospace Conference, 2016, pp. 1–13. doi:10.1109/AERO.2016.7500793.
K. Kolcio, L. Fesq, and R. Mackey, “Model‒based approach to rover health assessment for increased productivity”, in 2017 IEEE Aerospace Conference, 2017, pp. 1–13. doi: 10.1109/AERO.2017.7943835.
A. Nikora, P. Srivastava, L. Fesq, S. Chung, and K. Kolcio, “Assurance of model‒based fault diagnosis”, in 2018 IEEE Aerospace Conference, 2018, pp. 1–14. doi: 10.1109/AERO. 2018.8396550.
G. B. Aaseng, “Scaling up model‒based diagnostic and fault effects reasoning for spacecraft”, in AIAA SPACE 2015 Conference and Exposition, 2015, p. 4465.
D. Henry, A. A. Barahona, N. Ellero, V. Dubanchet, P. Colmenarejo, and F. Ankersen, “Model‒based fault diagnosis and fault‒tolerant data fusion algorithms for the ESA’s In‒Orbit‒Assembly project”, in 2021 5th International Conference on Control and Fault‒Tolerant Systems (SysTol), 2021, pp. 211–218. doi: 10.1109/SysTol52990.2021.9595822.
D. Henry, C. Le Peuvédic, L. Strippoli, and F. Ankersen, “Model‒based FDIR and Fault Accommodation for a Ren‒ dezvous Mission around the Mars Planet: the Mars Sample Return Case”, IFAC‒PapersOnLine, vol. 49, no. 5, pp. 266– 271, 2016, doi: https://doi.org/10.1016/j.ifacol.2016.07.124.
D. Henry et al., “Model‒based fault diagnosis and tolerant control: the ESA's e.Deorbit mission”, in 2019 18th Euro‒ pean Control Conference (ECC), 2019, pp. 4356–4361. doi: 10.23919/ECC.2019.8796282.
R. Fonod, D. Henry, C. Charbonnel, and E. Bomschlegl, “A class of nonlinear unknown input observer for fault diagnosis: Application to fault tolerant control of an autonomous space‒ craft”, in 2014 UKACC International Conference on Control (CONTROL), 2014, pp. 13–18. doi: 10.1109/CONTROL. 2014.6915108.
Y. Gao, T. Yang, N. Xing, and M. Xu, “Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines”, in 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2012, pp. 1984–1988. doi: 10.1109/ICIEA.2012.6361054.
S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis”, Chemometrics and Intelligent Laboratory Systems, vol. 2, no. 1–3, pp. 37–52, 1987.
C. Cortes and V. Vapnik, “Support‒vector networks”, Machine learning, vol. 20, pp. 273–297, 1995.
K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding”, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD '18. London, United Kingdom: Association for Computing Machinery, 2018, p. 387. doi: 10.1145/3219819.3219845.
S. Hochreiter and J. Schmidhuber, “Long Short‒term Memory”, Neural computation, vol. 9, pp. 1735–80, 1997, doi: 10.1162/neco.1997.9.8.1735
I. Gueddi, O. Nasri, K. Benothman, and P. Dague, “Fault detection and isolation of spacecraft thrusters using an extended principal component analysis to interval data”, International Journal of Control, Automation and Systems, vol. 15, pp. 776– 789, 2017.
J. R. Mansell and D. A. Spencer, “Deep learning fault diagno‒ sis for spacecraft attitude determination and control”, Journal of Aerospace Information Systems, vol. 18, no. 3, pp. 102– 115, 2021.
B. Xiao and S. Yin, “A Deep Learning Based Data‒Driven Thruster Fault Diagnosis Approach for Satellite Attitude Con‒ trol System”, IEEE Transactions on Industrial Electronics, vol. 68, no. 10, pp. 10162–10170, 2021, doi: 10.1109/TIE. 2020.3026272.
M. Yamamoto et al., “Surge Pressure Management in HTV Propulsion System”, in 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 2007.
B. Brindle, J. Gilbert, J. Moore, and G. Risha, “Effects of Water Hammer on Propulsion Systems”, 2016, p. [24] E. B. Wylie, V. L. Streeter, and L. Suo, Fluid transients in sys‒ tems, vol. 1. Prentice Hall Englewood Cliffs, NJ, 1993.
A. Kodura, “An analysis of the impact of valve closure time on the course of water hammer”, Archives of Hydro‒Engineering and Environmental Mechanics, vol. 63, no. 1, 2016.
A. Kumar, P. S. Prasad, and M. R. Rao, “Experimental studies of water hammer in propellant feed system of reaction control system”, Propulsion and Power Research, vol. 7, no. 1, pp. 52–59, 2018.
R. Lecourt and J. Steelant, “Experimental investigation of wa‒ terhammer in simplified feed lines of satellite propulsion systems”, Journal of Propulsion and Power, vol. 23, no. 6, pp. 1214–1224, 2007.
K. Kawatsu et al., “Resilient redundant spacecraft GN&C sys‒ tem fault detection and diagnostics”, in Aerospace Europe Conference, AEC2, 2020.
K. Kawatsu, C. Inoue, Y. Terauchi, Y. Daimon, G. Fujii, and A. Noumi, “System‒level Integrated Modeling and Simulation Targeting Space Propulsion System for Design Evaluation and Risk Assessment”, in 32nd International Symposium on Space Technology and Science, 2019.
G. Fujii et al., “Verification of the Transient Behavior of the SLIM Propulsion System (Smart Lander for Investigating Moon)”, in AIAA Propulsion and Energy 2021 Forum, 2021, p. 3582.
H. YAMAMOTO, K. KAWATSU, Y. DAIMON, and G. FUJII, “Development of a Dynamic Response Model Considering Trapped Gas Effect for Spacecraft Liquid Propulsion Systems”, Journal of Evolving Space Activities, vol. 1, no., p. 34, 2023, doi: 10.57350/jesa.34.
K. KAWATSU, Y.‒i. IZATO, and A. MIYAKE, “Framework for Model‒Based Approach to Life Cycle Risk Management for Spacecraft Liquid Propulsion System”, Journal of Evolving Space Activities, vol. 1, p. 78, 2023.
K. Kawatsu, G. Fujii, T. Nagata, H. Yamamoto, and Y. Dai‒ mon, “Preliminary Demonstration of Concept for Fault Diagnosis in Resilient Redundant Spacecraft Propulsion”, in AIAA SCITECH 2023 Forum, 2023, p. 1259.
K. Tominaga et al., “Dataset Generation Based on 1D‒CAE Modeling for Fault Diagnostics in a Spacecraft Propulsion System”, in PHM Society Asia‒Pacific Conference, 2023.
T. Minami and J. Lee, “PHM for Spacecraft Propulsion Sys‒ tems: Similarity‒Based Model and Physics‒Inspired Features”, in PHM Society Asia‒Pacific Conference, 2023.
S. K. Lee et al., “Hybrid Approach of XGBoost and Rule‒based Model for Fault Detection and Severity Estimation in Spacecraft Propulsion System”, in PHM Society Asia‒Pacific Conference, 2023.
Y. Kato, T. Kato, and T. Tanaka, “Anomaly Detection in Space‒ craft Propulsion System using Time Series Classification based on K‒NN”, in PHM Society Asia‒Pacific Conference, 2023.
B. Schölkopf, J. C. Platt, J. Shawe‒Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high‒dimensional distribution”, Neural computation, vol. 13, no. 7, pp. 1443– 1471, 2001.
E. Fix and J. L. Hodges, “Discriminatory analysis. Nonparametric discrimination: Consistency properties”, International Statistical Review/Revue Internationale de Statistique, vol. 57, no. 3, pp. 238–247, 1989.
S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient algorithms for mining outliers from large data sets”, in Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, pp. 427–438.
P. C. Mahalanobis, “On the generalized distance in statistics”, Sankhyā: The Indian Journal of Statistics, Series A (2008‒), vol. 80, p. S1‒‒S7, 2018.
C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning, vol. 4, no. 4. Springer, 2006.
B. Awwad Shiekh Hasan and J. Q. Gan, “Sequential EM for unsupervised adaptive Gaussian mixture model based classifier”, in International Workshop on Machine Learning and Data Mining in Pattern Recognition, 2009, pp. 96–106.
P. Karsmakers, K. Pelckmans, and J. A. K. Suykens, “Multi‒ class kernel logistic regression: a fixed‒size implementation”, in 2007 International Joint Conference on Neural Networks, 2007, pp. 1756–1761. doi: 10.1109/IJCNN.2007.4371223.
A. Widodo and B.‒S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis”, Mechanical systems and signal processing, vol. 21, no. 6, pp. 2560–2574, 2007.
J. Platt, “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods”, Adv. Large Margin Classif., vol. 10, p., 2000.
I. Guyon and A. Elisseeff, “An introduction to variable and feature selection”, Journal of machine learning research, vol. 3, no. Mar, pp. 1157–1182, 2003.
S. Bouatmane, M. A. Roula, A. Bouridane, and S. Al‒Maadeed, “Round‒Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispec‒ tral imagery”, Machine Vision and Applications, vol. 22, pp. 865–878, 2011.
S. Shafiee, L. M. Lied, I. Burud, J. A. Dieseth, M. Alsheikh, and M. Lillemo, “Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery”, Computers and Electronics in Agriculture, vol. 183, p. 106036, 2021, doi: https://doi.org/10.1016/j.compag.2021.106036.
Raspberry Pi Foundation, “Raspberry Pi Zero W”. 2023.
J. Danos, C. Page, S. Jones, and B. Lewis, “USU’s GASPACS CubeSat”, 2022.
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Technical Papers