Lean Demonstration of On-Board Thermal Anomaly Detection Using Machine Learning

Abstract

Moore’s law states that the performance of computers doubles about every two years. This has dramatic consequences for any modern high development and for satellites. The long development cycles cause these expensive assets to be obsolete before the start of their operations. The advancement also presents challenges to their design, particularly from a thermal perspective, as more heat is dissipated and circuits are more fragile. These challenges mandate that faster spacecraft development methods are found and thermal management technologies are developed. We elaborate on existing development methodologies and present our own lean method. We explore the development of a thermal anomaly-detection payload, extending from conception to in-orbit commissioning, to stimulate discussions on space hardware development approaches. The payload consists of four miniaturized infrared cameras, heating sources in view of the cameras simulating an anomaly, an on-board processor, and peripherals for electrical and communication interfaces. The paper outlines our methodology and its application, showcasing the success of our efforts with the first-light activation of our cameras in orbit. We show our lean method, featuring reference technical and management models, from which we derive further development tools; such details are normally not available in the scientific-engineering literature. Additionally, we address the shortcomings identified during our development, such as the failure of an on-board component and propose improvements for future developments.