American Institute of Mathematical Sciences

doi: 10.3934/mfc.2022020
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Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

 Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing 100190, China

*Corresponding author: Chengrui Liu

Received  December 2021 Revised  March 2022 Early access July 2022

Fund Project: This work was supported by the National Natural Science Funds for Excellent Young Scholars of China under Grant 62022013

Satellite networking, as the future development direction of aero-space, requires high-precision autonomous fault diagnosis capability for a single satellite. In this paper, aiming at the characteristics of closed-loop fault propagation and high data dimensionality of spacecraft control system, neural network algorithms are conducted to study the fault diagnosis of spacecraft high-dimensional coupled data. Based on the ground test data of a certain spacecraft, this paper converts the high-dimensional sequence data into grayscale images, and then uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to diagnose them respectively. The effectiveness of the methods in this paper is illustrated by comparing and validating them with three non-image-based machine learning algorithms, namely, K-NearestNeighbor, Bayesian classifier, and K-NearestNeighbor based on Principal Component Analysis.

Citation: Hanyu Liang, Chengrui Liu, Wenjing Liu, Wenbo Li, Heyu Xu. Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping. Mathematical Foundations of Computing, doi: 10.3934/mfc.2022020
References:
 [1] S. Ai, J. Song and G. Cai, A real-time fault diagnosis method for hypersonic air vehicle with sensor fault based on the auto temporal convolutional network, Aerospace Science and Technology, 119 (2021), 107220.  doi: 10.1016/j.ast.2021.107220. [2] D. Bergman, B. Glass, K. Zacny and G. Paulsen, Using Distributed Transfer Function method (DTFM) for autonomous health monitoring of interplanetary drills, AIAA SPACE 2015 Conference and Exposition, Pasadena, CA, USA, (2015), 2015–4482. doi: 10.2514/6.2015-4482. [3] S. Belagoune, N. Bali, A. Bakdi, B. Baadji and K. Atif, Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems, Measurement, 117 (2021), 109330.  doi: 10.1016/j.measurement.2021.109330. [4] G. J. Clark, W. Eddy, S. K. Johnson, J. Barnes and D. Brooks, Architecture for cognitive networking within NASA's future space communications infrastructure, 34th AIAA International Communications Satellite Systems Conference, (2016), 2016–5725. doi: 10.2514/6.2016-5725. [5] F. Cheng, X. Guo, Y. Qi, J. Xu, W. Qiu, Z. Zhang, W. Zhang and N. Qi, Research on satellite power anomaly detection method based on LSTM, 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), (2021), 20528754. doi: 10.1109/ICPECA51329.2021.9362601. [6] M. Daigle, A. Bregon and I. Roychoudhury, Qualitative event-based diagnosis with possible conflicts applied to spacecraft power distribution systems, IFAC Proceedings Volumes, 45 (2012), 265-270.  doi: 10.3182/20120829-3-MX-2028.00084. [7] A. Falcoz, F. Boquet, M. Dinh, B. Polle, G. Flandin and E. Bornschlegl, Robust fault diagnosis strategies for spacecraft application to LISA Pathfinder experiment, IFAC Proceedings Volumes, 43 (2010), 404-409.  doi: 10.3182/20100906-5-JP-2022.00069. [8] D. Gao, Y. Zhu, Z. Ren, K. Yan and W. Kang, A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity, Knowledge-Based Systems, 231 (2021), 107413.  doi: 10.1016/j.knosys.2021.107413. [9] M. Ganesan, R. Lavanya and M. Nirmala Devi, Fault detection in satellite power system using convolutional neural network, Telecommunication Systems, 76 (2021), 505-511.  doi: 10.1007/s11235-020-00722-5. [10] Q. Hu, G. Niu and C. Wang, Spacecraft attitude fault-tolerant control based on iterative learning observer and control allocation, Aerospace Science and Technology, 75 (2018), 245-253. [11] D. Henry, C. Le Peuvédic, L. Strippoli and F. Ankersen, Robust model-based fault diagnosis of thruster faults in spacecraft, IFAC-PapersOnLine, 48 (2015), 1078-1083.  doi: 10.1016/j.ifacol.2015.09.670. [12] Y. Hua, L. Mou and X. Zhu, Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification, ISPRS Journal of Photogrammetry and Remote Sensing, 149 (2019), 188-199.  doi: 10.1016/j.isprsjprs.2019.01.015. [13] M. S. Islam and A. Rahimi, Fault prognosis of satellite reaction wheels using a two-step LSTM network, 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), (2021). [14] Y. Li, Q. Hu and X. Shao, Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros, Chinese Journal of Aeronautics, 35 (2021), 261-273.  doi: 10.1016/j.cja.2021.11.020. [15] J. Li, Y. Liu and Q. Li, Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method, Measurement, 189 (2021), 110500.  doi: 10.1016/j.measurement.2021.110500. [16] Y. Li, X. Du, F. Wan, X. Wang and H. Yu, Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging, Chinese Journal of Aeronautics, 33 (2020), 427-438.  doi: 10.1016/j.cja.2019.08.014. [17] Y. Liu, Q. Pan, H. Wang and T. He, Fault diagnosis of satellite flywheel bearing based on convolutional neural network, 2019 Prognostics and System Health Management Conference (PHM-Qingdao), (2019), 8942957. doi: 10.1109/PHM-Qingdao46334.2019.8942957. [18] D. Long, X. Wen, J. Wang and B. Wei, A data fusion fault diagnosis method based on LSTM and DWT for satellite reaction flywheel, Mathematical Problems in Engineering, 2020 (2020), 2893263.  doi: 10.1155/2020/2893263. [19] Z. Meng, Y. Zhang, B. Zhu, Z. Pan, L. Cui, J. Li and F. Fan, Research on rolling bearing fault diagnosis method based on ARMA and optimized MOMEDA, Measurement, 189 (2021), 110465.  doi: 10.1016/j.measurement.2021.110465. [20] H. Ruan, Y. Wang, X. Li, Y. Qin and B. Tang, An enhanced non-local weakly supervised fault diagnosis method for rotating machinery, Measurement, 189 (2021), 110433.  doi: 10.1016/j.measurement.2021.110433. [21] J. Shi, D. Peng, Z. Peng, Z. Zhang, L. Goebol and D. Wu, Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks, Mechanical Systems and Signal Processing, 162 (2022), 107996.  doi: 10.1016/j.ymssp.2021.107996. [22] S. Tariq, S. Lee and Y. Shin, M. S. Lee, O. Jung, D. Chung and S. S. Woo, Detecting anomalies in space using multivariate convolutional LSTM with mixtures of probabilistic PCA, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2019), 3330776. doi: 10.1145/3292500.3330776. [23] R. Wang, Y. Cheng and M. Xu, Analytical redundancy based fault diagnosis scheme for satellite attitude control systems, Journal of the Franklin Institute, 352 (2015), 1906-1931.  doi: 10.1016/j.jfranklin.2015.02.003. [24] Y. Xia, J. Zhou, T. Xu and W. Gao, An improved deep convolutional neural network model with kernel loss function in image classification, Mathematical Foundations of Computing, 3 (2020), 51-64.  doi: 10.3934/mfc.2020005. [25] J. Xu, L. Zhou, W. Zhao, Y. Fan and X. Ding, Zero-shot learning for compound fault diagnosis of bearings, Expert Systems with Applications, 190 (2022), 116197.  doi: 10.1109/IJCNN52387.2021.9534279. [26] B. Xiao and S. Yin, A deep learning-based data-driven thruster fault diagnosis approach for satellite attitude control system, IEEE Transactions on Industrial Electronics, 68 (2020), 10162-10170.  doi: 10.1109/TIE.2020.3026272. [27] Z. Zhang, S. Li, J. Lu, Y. Xin and H. Ma, Intrinsic component filtering for fault diagnosis of rotating machinery, Chinese Journal of Aeronautics, 34 (2021), 397-409.  doi: 10.1016/j.cja.2020.07.019. [28] C. Zhang, Y. Liu, F. Wan, B. Chen, J. Liu and B. Hu, Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases, Chinese Journal of Aeronautics, 33 (2020), 407-417.  doi: 10.1016/j.cja.2019.03.014. [29] J. Zhang, Y. Sun, L. Guo, H. Gao, X. Hong and H. Song, A new bearing fault diagnosis method based on modified convolutional neural networks, Chinese Journal of Aeronautics, 33 (2020), 439-447.  doi: 10.1016/j.cja.2019.07.011. [30] F. Zhou, R. Hang, Q. Liu and X. Yuan, Hyperspectral image classification using spectral-spatial LSTMs, CCCV 2017: Computer Vision, 771 (2017), 557-558.  doi: 10.1007/978-981-10-7299-4_48. [31] Q. Zhang, J. Zhang, J. Zou and S. Fan, A novel fault diagnosis method based on stacked LSTM, IFAC-PapersOnLine, 53 (2020), 790-795.  doi: 10.1016/j.ifacol.2020.12.832. [32] F. Zhao, Z. Zhang, M. Hu, Y. Deng and X. Shen, Exo-atmospheric infrared objects classification based on dual-channel LSTM network, Infrared Physics & Technology, 111 (2020), 103535.  doi: 10.1016/j.infrared.2020.103535.

show all references

References:
 [1] S. Ai, J. Song and G. Cai, A real-time fault diagnosis method for hypersonic air vehicle with sensor fault based on the auto temporal convolutional network, Aerospace Science and Technology, 119 (2021), 107220.  doi: 10.1016/j.ast.2021.107220. [2] D. Bergman, B. Glass, K. Zacny and G. Paulsen, Using Distributed Transfer Function method (DTFM) for autonomous health monitoring of interplanetary drills, AIAA SPACE 2015 Conference and Exposition, Pasadena, CA, USA, (2015), 2015–4482. doi: 10.2514/6.2015-4482. [3] S. Belagoune, N. Bali, A. Bakdi, B. Baadji and K. Atif, Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems, Measurement, 117 (2021), 109330.  doi: 10.1016/j.measurement.2021.109330. [4] G. J. Clark, W. Eddy, S. K. Johnson, J. Barnes and D. Brooks, Architecture for cognitive networking within NASA's future space communications infrastructure, 34th AIAA International Communications Satellite Systems Conference, (2016), 2016–5725. doi: 10.2514/6.2016-5725. [5] F. Cheng, X. Guo, Y. Qi, J. Xu, W. Qiu, Z. Zhang, W. Zhang and N. Qi, Research on satellite power anomaly detection method based on LSTM, 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), (2021), 20528754. doi: 10.1109/ICPECA51329.2021.9362601. [6] M. Daigle, A. Bregon and I. Roychoudhury, Qualitative event-based diagnosis with possible conflicts applied to spacecraft power distribution systems, IFAC Proceedings Volumes, 45 (2012), 265-270.  doi: 10.3182/20120829-3-MX-2028.00084. [7] A. Falcoz, F. Boquet, M. Dinh, B. Polle, G. Flandin and E. Bornschlegl, Robust fault diagnosis strategies for spacecraft application to LISA Pathfinder experiment, IFAC Proceedings Volumes, 43 (2010), 404-409.  doi: 10.3182/20100906-5-JP-2022.00069. [8] D. Gao, Y. Zhu, Z. Ren, K. Yan and W. Kang, A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity, Knowledge-Based Systems, 231 (2021), 107413.  doi: 10.1016/j.knosys.2021.107413. [9] M. Ganesan, R. Lavanya and M. Nirmala Devi, Fault detection in satellite power system using convolutional neural network, Telecommunication Systems, 76 (2021), 505-511.  doi: 10.1007/s11235-020-00722-5. [10] Q. Hu, G. Niu and C. Wang, Spacecraft attitude fault-tolerant control based on iterative learning observer and control allocation, Aerospace Science and Technology, 75 (2018), 245-253. [11] D. Henry, C. Le Peuvédic, L. Strippoli and F. Ankersen, Robust model-based fault diagnosis of thruster faults in spacecraft, IFAC-PapersOnLine, 48 (2015), 1078-1083.  doi: 10.1016/j.ifacol.2015.09.670. [12] Y. Hua, L. Mou and X. Zhu, Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification, ISPRS Journal of Photogrammetry and Remote Sensing, 149 (2019), 188-199.  doi: 10.1016/j.isprsjprs.2019.01.015. [13] M. S. Islam and A. Rahimi, Fault prognosis of satellite reaction wheels using a two-step LSTM network, 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), (2021). [14] Y. Li, Q. Hu and X. Shao, Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros, Chinese Journal of Aeronautics, 35 (2021), 261-273.  doi: 10.1016/j.cja.2021.11.020. [15] J. Li, Y. Liu and Q. Li, Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method, Measurement, 189 (2021), 110500.  doi: 10.1016/j.measurement.2021.110500. [16] Y. Li, X. Du, F. Wan, X. Wang and H. Yu, Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging, Chinese Journal of Aeronautics, 33 (2020), 427-438.  doi: 10.1016/j.cja.2019.08.014. [17] Y. Liu, Q. Pan, H. Wang and T. He, Fault diagnosis of satellite flywheel bearing based on convolutional neural network, 2019 Prognostics and System Health Management Conference (PHM-Qingdao), (2019), 8942957. doi: 10.1109/PHM-Qingdao46334.2019.8942957. [18] D. Long, X. Wen, J. Wang and B. Wei, A data fusion fault diagnosis method based on LSTM and DWT for satellite reaction flywheel, Mathematical Problems in Engineering, 2020 (2020), 2893263.  doi: 10.1155/2020/2893263. [19] Z. Meng, Y. Zhang, B. Zhu, Z. Pan, L. Cui, J. Li and F. Fan, Research on rolling bearing fault diagnosis method based on ARMA and optimized MOMEDA, Measurement, 189 (2021), 110465.  doi: 10.1016/j.measurement.2021.110465. [20] H. Ruan, Y. Wang, X. Li, Y. Qin and B. Tang, An enhanced non-local weakly supervised fault diagnosis method for rotating machinery, Measurement, 189 (2021), 110433.  doi: 10.1016/j.measurement.2021.110433. [21] J. Shi, D. Peng, Z. Peng, Z. Zhang, L. Goebol and D. Wu, Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks, Mechanical Systems and Signal Processing, 162 (2022), 107996.  doi: 10.1016/j.ymssp.2021.107996. [22] S. Tariq, S. Lee and Y. Shin, M. S. Lee, O. Jung, D. Chung and S. S. Woo, Detecting anomalies in space using multivariate convolutional LSTM with mixtures of probabilistic PCA, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2019), 3330776. doi: 10.1145/3292500.3330776. [23] R. Wang, Y. Cheng and M. Xu, Analytical redundancy based fault diagnosis scheme for satellite attitude control systems, Journal of the Franklin Institute, 352 (2015), 1906-1931.  doi: 10.1016/j.jfranklin.2015.02.003. [24] Y. Xia, J. Zhou, T. Xu and W. Gao, An improved deep convolutional neural network model with kernel loss function in image classification, Mathematical Foundations of Computing, 3 (2020), 51-64.  doi: 10.3934/mfc.2020005. [25] J. Xu, L. Zhou, W. Zhao, Y. Fan and X. Ding, Zero-shot learning for compound fault diagnosis of bearings, Expert Systems with Applications, 190 (2022), 116197.  doi: 10.1109/IJCNN52387.2021.9534279. [26] B. Xiao and S. Yin, A deep learning-based data-driven thruster fault diagnosis approach for satellite attitude control system, IEEE Transactions on Industrial Electronics, 68 (2020), 10162-10170.  doi: 10.1109/TIE.2020.3026272. [27] Z. Zhang, S. Li, J. Lu, Y. Xin and H. Ma, Intrinsic component filtering for fault diagnosis of rotating machinery, Chinese Journal of Aeronautics, 34 (2021), 397-409.  doi: 10.1016/j.cja.2020.07.019. [28] C. Zhang, Y. Liu, F. Wan, B. Chen, J. Liu and B. Hu, Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases, Chinese Journal of Aeronautics, 33 (2020), 407-417.  doi: 10.1016/j.cja.2019.03.014. [29] J. Zhang, Y. Sun, L. Guo, H. Gao, X. Hong and H. Song, A new bearing fault diagnosis method based on modified convolutional neural networks, Chinese Journal of Aeronautics, 33 (2020), 439-447.  doi: 10.1016/j.cja.2019.07.011. [30] F. Zhou, R. Hang, Q. Liu and X. Yuan, Hyperspectral image classification using spectral-spatial LSTMs, CCCV 2017: Computer Vision, 771 (2017), 557-558.  doi: 10.1007/978-981-10-7299-4_48. [31] Q. Zhang, J. Zhang, J. Zou and S. Fan, A novel fault diagnosis method based on stacked LSTM, IFAC-PapersOnLine, 53 (2020), 790-795.  doi: 10.1016/j.ifacol.2020.12.832. [32] F. Zhao, Z. Zhang, M. Hu, Y. Deng and X. Shen, Exo-atmospheric infrared objects classification based on dual-channel LSTM network, Infrared Physics & Technology, 111 (2020), 103535.  doi: 10.1016/j.infrared.2020.103535.
The AOCS of spacecraft
The closed-loop characteristic of spacecraft fault
Mapping of high-dimensional data to pattern data
The architecture of CNN
The structure diagram of Long Short-Term Memory network gate unit
The image processing principle of LSTM
The architecture of LSTM
2D visualization image of Dataset1
2D visualization image of Dataset2
The experimental fault diagnosis results on Dataset 1
Confusion matrixes of each model
The fault diagnosis results for mixed faults
Confusion matrixes of each model for mixed faults
Hyperparameters of CNN
 Notation Description Kernel size Stride Kernel number Input Input data 9$\times$9 $\backslash$ $\backslash$ Conv1 Convolution 3$\times$3 1$\times$1 32 P1 Max pooling 1$\times$1 1$\times$1 32 Conv2 Convolution 3$\times$3 1$\times$1 64 P2 Max pooling 3$\times$3 3$\times$3 64 F Fully connected 576$\times$1 $\backslash$ $\backslash$
 Notation Description Kernel size Stride Kernel number Input Input data 9$\times$9 $\backslash$ $\backslash$ Conv1 Convolution 3$\times$3 1$\times$1 32 P1 Max pooling 1$\times$1 1$\times$1 32 Conv2 Convolution 3$\times$3 1$\times$1 64 P2 Max pooling 3$\times$3 3$\times$3 64 F Fully connected 576$\times$1 $\backslash$ $\backslash$
Fault mode
 Number Fault mode Fault device 1 Constant deviance fault of CMG The gimbal angular velocity of the 2nd CMG 2 Noise increase fault of gyroscope The 1st gyroscope 3 Saturation fault of gyroscope The 3rd gyroscope 4 Constant deviance fault of gyroscope output The 4th gyroscope 5 Constant fault of gyroscope output The 4th gyroscope
 Number Fault mode Fault device 1 Constant deviance fault of CMG The gimbal angular velocity of the 2nd CMG 2 Noise increase fault of gyroscope The 1st gyroscope 3 Saturation fault of gyroscope The 3rd gyroscope 4 Constant deviance fault of gyroscope output The 4th gyroscope 5 Constant fault of gyroscope output The 4th gyroscope
Fault type
 Number in Dataset 2 Fault mode Fault device and its amplitude 6 Chord width over-tolerance of infrared earth sensor The 1st infrared earth sensor and its chord width is 4.538. 7 The 1st infrared earth sensor and its chord width is 3. 8 Ground entry angle over-tolerance of infrared earth sensor The 2nd infrared earth sensor and its ground entry angle is 2.793°. 9 The 2nd infrared earth sensor and its ground entry angle is 1.5°.
 Number in Dataset 2 Fault mode Fault device and its amplitude 6 Chord width over-tolerance of infrared earth sensor The 1st infrared earth sensor and its chord width is 4.538. 7 The 1st infrared earth sensor and its chord width is 3. 8 Ground entry angle over-tolerance of infrared earth sensor The 2nd infrared earth sensor and its ground entry angle is 2.793°. 9 The 2nd infrared earth sensor and its ground entry angle is 1.5°.
Model parameter setting of non-image-based algorithms
 Name Setting KNN $k$=4Distance formula: Euclidean distance NB Gaussian Bayes PCA+KNN PCA: Remain 99.9% of featuresKNN: $k$=4, Distance formula: Euclidean distance
 Name Setting KNN $k$=4Distance formula: Euclidean distance NB Gaussian Bayes PCA+KNN PCA: Remain 99.9% of featuresKNN: $k$=4, Distance formula: Euclidean distance
Results of Dataset 1
 Name Accuracy $\pm$ standard deviation KNN 98.61$\pm$0.32 NB 92.83$\pm$0.68 PCA+KNN 88.10$\pm$0.79 CNN 100.00$\pm$0.01 LSTM 99.96$\pm$0.69
 Name Accuracy $\pm$ standard deviation KNN 98.61$\pm$0.32 NB 92.83$\pm$0.68 PCA+KNN 88.10$\pm$0.79 CNN 100.00$\pm$0.01 LSTM 99.96$\pm$0.69
Model parameter updated setting of non-image-based algorithms
 Name Setting KNN $k$=3 Distance formula: Euclidean distance NB Gaussian Bayes PCA+KNN PCA: Remain 99.99999% of features KNN: $k$=3,Distance formula: Euclidean distance
 Name Setting KNN $k$=3 Distance formula: Euclidean distance NB Gaussian Bayes PCA+KNN PCA: Remain 99.99999% of features KNN: $k$=3,Distance formula: Euclidean distance
Mixed fault results
 Name Accuracy $\pm$ standard deviation KNN 91.59$\pm$0.43 NB 98.10$\pm$0.21 PCA+KNN 84.47$\pm$0.53 CNN 100.00$\pm$0.06 LSTM 99.86$\pm$1.87
 Name Accuracy $\pm$ standard deviation KNN 91.59$\pm$0.43 NB 98.10$\pm$0.21 PCA+KNN 84.47$\pm$0.53 CNN 100.00$\pm$0.06 LSTM 99.86$\pm$1.87
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