Fault detection in satellite power system using convolutional neural network

• Published in: Telecommunication systems Vol. 76; no. 4; pp. 505 - 511
• Main Authors: Ganesan, M, Lavanya, R, Nirmala Devi, M
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2021; Springer Nature B.V
• Subjects: Algorithms; Anomalies; Artificial Intelligence; Artificial neural networks; Business and Management; Computer Communication Networks; Data processing; False alarms; Fault detection; Fault diagnosis; IT in Business; Neural networks; Probability Theory and Stochastic Processes; Sensors; Spacecraft power supplies; Telecommunications systems; Telemetry; Fault diagnosis; Stockwell transform; Satellite power system; Convolutional neural network
• ISSN: 1018-4864; 1572-9451
• DOI: 10.1007/s11235-020-00722-5

Satellite failures account for heavy, irreparable damages, especially when associated with the Power System which is the heart of a satellite. Anomalies in Satellite Power System (SPS) can lead to complete failure of the mission. This demands the need to understand the causes of power system related failures. Huge number of sensors installed in a satellite system conveys information regarding the health of the system. The conventional manual level checking of sensors can be augmented with data driven fault diagnosis approach to reduce the false alarm and burden on operating personnel. The latter has the advantage of exploiting the interrelationship between sensor measurements for fault diagnosis. In this work, Convolutional Neural Network (CNN) is trained on satellite telemetry data for sensor fault detection in SPS. Various processing schemes in time and frequency domains were explored to process the input data to CNN. Promising results were obtained with combination of Stockwell transform (S-transform) and CNN for data processing and classification, respectively. Advanced Diagnostics and Prognostics Testbed (ADAPT), a publicly-available dataset was analysed and used for validating the proposed algorithm, yielding an accuracy as high as 96.7%, precisison of 0.9, F1 score of 0.95 and AUC equal to 0.976.