Feature-weighted Random Forest with Boruta for Fault Diagnosis of Satellite Attitude Control Systems
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The performance of random forest (RF) based satellite attitude control system (ACS) fault diagnosis methods is limited by uninformative features in high-dimensional data. To solve this problem, we proposed a feature-weighted random forest with Boruta (FWRFB) based fault diagnosis method is proposed for fault diagnosis of ACSs. Firstly, a Boruta feature selection algorithm is used to obtain a feature set and determine significant feature weights. Subsequently, a novel feature-weighted random forest (FWRF) algorithm is designed, which utilizes feature-weighted random sampling instead of simple random sampling to generate feature subsets in the RF. The FWRFB effectively utilizes the feature information while mitigating noise interference. Finally, a FWRFB-based diagnostic module is developed for online fault diagnosis of ACSs. The effectiveness of the proposed method is verified by the ACS data from a semi-physical simulation platform.
How to Cite
##plugins.themes.bootstrap3.article.details##
Feature-weighted random forest, Fault diagnosis, Satellite attitude control system, Boruta
Chen, H., Hu, C., Han, B., & Miao, K. (2024). A method of diagnosing analog circuit soft faults using boruta features and light gbm. Electronics, 13(6), 1123.
Chen, S., Yang, R., Zhong, M., Xi, X., & Liu, C. (2023). A random forest and model-based hybrid method of fault diagnosis for satellite attitude control systems. IEEE Transactions on Instrumentation and Measurement, 72, 3518413.
Eroglu, D. Y., & Akcan, U. (2024). An adapted ant colony optimization for feature selection. Applied Artificial Intelligence, 38(1), 2335098.
Ghosh, D., & Cabrera, J. (2022). Enriched random forest for high dimensional genomic data. IEEE/ACM Transactions On Computational Biology And Bioinformatics, 19(5), 2817-2828.
Guo, L., Cao, W., Bai, L., Zhang, J., Xing, L., Xiang, E., & Zhou, L. (2021). Fault diagnosis based on multiscale texture features of cable terminal on emu of highspeed railway. IEEE Transactions on Instrumentation and Measurement, 70, 3502612.
Huang, J., Liu, Y., Zhong, M., Yang, R., Li, W., & Liu, C. (2021). Fault diagnosis of satellite attitude control systems using random forest algorithm. Journal of Astronautics (in Chinese), 42(4), 513-521.
Ji, N., Zhang, Q., & Liu, J. (2024). Event-triggered adaptive vibration control for a flexible satellite with time varying actuator faults. Neurocomputing, 584, 127578. Leo, B. (2001). Random forest. Machine Learning, 45(1), 5-32.
Liu, Y., & Zhao, H. (2017). Variable importance-weighted random forests. Quantitative Biology, 5(4), 338-351.
Papakonstantinou, C., Daramouskas, I., Lappas, V., Moulianitis, V. C., & Kostopoulos, V. (2022). Determination of threshold failure levels of semiconductor diodes and transistors due to pulse voltages. Aerospace, 9(9), 164.
Pourtakdoust, S. H., Mehrjardi, M. F., & Hajkarim, M. H. (2022). Attitude estimation and control based on modified unscented kalman filter for gyro-less satellite with faulty sensors. Acta Astronautica, 191, 134-147.
Sun, Z., Wang, G., Li, P., Wang, H., Zhang, M., & Liang, X. (2011). An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications, 237(Part B), 121549.
Suo, M., Zhu, B., & Yu, Z. (2019). Data-driven fault diagnosis of satellite power system using fuzzy bayes risk and svm. Aerospace Science and Technology, 84, 1092- 1105.
Wu, F., Chen, K., Qiu, G., & Zhou,W. (2024). Determination of threshold failure levels of semiconductor diodes and transistors due to pulse voltages. IEEE Transactions on Industrial Electronics, 1-11.
Xiao, B., & Yin, S. (2021). A deep learning-based data driven thruster fault diagnosis approach for satellite attitude control system. IEEE Transactions on Industrial Electronics, 68(10), 10162-10170.
Yang, R., & Zhong, M. (Eds.). (2022). Machine learning based fault diagnosis for industrial engineering systems. CRC Press.
Yu, C., Li, M., Wu, Z., Gao, K., & Wang, F. (2024). Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network. Measurement Science and Technology, 35(8), 086201.
Yuan, Z., Song, N., Pan, X., Song, J., & Ma, F. (2021). Fault detection, isolation, and reconstruction for satellite attitude sensors using an adaptive hybrid method. IEEE Transactions on Instrumentation and Measurement, 70, 3522112.
Zhong, M., Liu, C., Zhou, D., Li, W., & Xue, T. (2019). Probability analysis of fault diagnosis performance for satellite attitude control systems. IEEE Transactions on Industrial Informatics, 15(11), 5867-5876.
Zhu, Y., & Peng, H. (2022). Multiple random forests based intelligent location of single-phase grounding fault in power lines of dfig-based wind farm. Journal of Modern Power Systems and Clean Energy, 10(5), 1152- 1163.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.