A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels

• Published in: ISA transactions Vol. 51; no. 2; pp. 309 - 316
• Main Authors: Hu, Di, Sarosh, Ali, Dong, Yun-Feng
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2012; Elsevier
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Cluster Analysis; Computer science; control theory; systems; Computer Simulation; Control theory. Systems; Data Interpretation, Statistical; Data points; Diagnosis; Equipment Design; Equipment Failure; Exact sciences and technology; Fault detection and diagnosis; Fault diagnosis; Faults; Fundamental areas of phenomenology (including applications); Fuzzy clustering; Fuzzy Logic; Kernel fuzzy c-means clustering; Modelling and identification; Neural Networks (Computer); Physics; Reaction wheel; Reaction wheels; Reproducibility of Results; Satellite attitude control system; Similarity; Solid dynamics (ballistics, collision, multibody system, stabilization...); Solid mechanics; Spacecraft; Kernel fuzzy c-means clustering; Satellite attitude control system; Reaction wheel; Fuzzy clustering; Similarity; Fault detection and diagnosis; Wheel; Attitude control; Reaction force; Spacecraft; Solid dynamic; Satellite; Neural network; Similarity solution; Unsupervised learning; Fuzzy logic; Supervised learning; Classification; Fall; System identification; Learning algorithm; Fault diagnostic; Defect detection
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2011.10.005

Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the ‘known-fault’ classes while the low similarity points are labeled as ‘unknown-faults’. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%. ► With inspiration from the validity function, a novel KFCM method called C-KFCM is proposed in this paper. ► An effective analysis is adopted to choose suitable threshold for classifying the new data. ► The novel KFCM method has been successfully applied to unknown fault diagnosis of satellite reaction wheels. ► A comparison between C-KFCM and RBF illustrated that the new method is better than the supervised method.