Integrated system health management-based progressive diagnosis for space avionics

Abstract

Space avionics provides a spacecraft's essential capabilities and guarantees space flight safety and mission success. Integrated system health management (ISHM) was developed to deal with space avionics health management. Because space avionics structures are complex with both intangible and uncertain factors, it is difficult and often inefficient to directly carry out a detailed fault diagnosis throughout the avionics subsystem and subordinate modules. Furthermore, to date, little research has focused on efficient and effective space avionics fault diagnosis. This paper presents a novel ISHM-based progressive diagnosis methodology and framework, made up of a holistic state diagnosis at the subsystem level and a targeted fault diagnosis at the module level. An example is given to illustrate the methodology, which combines fuzziness and objective analysis with subjective judgments, using an enhanced fuzzy analytic hierarchal process with quantitative analytic methods, and incorporates intangibility and uncertainty, using a diagnostic Bayesian network with a learning algorithm for uncertain information. The methodology is demonstrated to show its ability to solve the diagnostic problems and is found to be applicable to space avionics systems of varying sizes. The demonstration further shows that the methodology is flexible enough to accommodate other efficient diagnostic approaches and fusion diagnostics.