Abstract
Satellite power system (SPS) is considered as the core of the satellite, where the faults occurring here adversely have an impact on the health of the satellite, thereby affecting the mission. This can be avoided by early detection of the faults occurring in the SPS. This work proposes a model to classify the faults present in the SPS using 2-dimensional convolutional neural network (2-D CNN) by encoding the multivariate time series data present in the ADAPT dataset into images. Encoding is done by using the methods such as Markov transition field (MTF), Gramian angular summation field (GASF), recurrence plot (RP), and spectrogram. Promising results were obtained using the GASF and 2-D CNN combination, which have yielded a test accuracy of 87.5%. The precision, recall, F1 score, and AUC score were 0.89, 0.854, 0.865, and 0.94, respectively.
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Hari Prasad, P., Jai Aakash, N.S., Avinash, T., Aravind, S., Ganesan, M., Lavanya, R. (2022). Fault Detection in SPS Using Image Encoding and Deep Learning. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_41
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DOI: https://doi.org/10.1007/978-981-16-3728-5_41
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