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Publication Type : Conference Paper
Publisher : Institute of Electrical and Electronics Engineers
Source : Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Institute of Electrical and Electronics Engineers Inc., p.814-817 (2019)
Keywords : Automated fault detection, Data driven technique, Fault detection, Instantaneous measurement, Kernel principal component analyses (KPCA), Multi Layer Perceptron, Multilayer neural networks, Multilayers, Multivariate time series classifications, NASA, Principal component analysis, Satellite power system, Satellites, Signal processing, Time series analysis, Time series techniques
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : Data driven techniques have become prominent in big data analysis. In this paper multisensory time series data is analyzed using Kernel Principal Component Analysis (KPCA) and Multilayer Perceptron (MLP) for fault detection in satellite power system. NASA's ADAPT dataset is used for validating the proposed algorithm. The proposed work differs from conventional time series techniques by considering each instantaneous measurement of multiple sensors as a data sample. This varied form of data augmentation results in improved fault diagnosis performance when compared to the conventional time series analysis. © 2019 IEEE.
Cite this Research Publication : S. Dheepadharshani, Anandh, S., Bhavinaya, K. B., and Dr. Lavanya R., “Multivariate time-series classification for automated fault detection in satellite power systems”, in Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 2019, pp. 814-817.