A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 40; no. 5; pp. 917 - 931
• Main Authors: Daigle, M J, Roychoudhury, I, Biswas, G, Koutsoukos, X D, Patterson-Hine, A, Poll, S
• Format: Journal Article
• Language: English
• Published: IEEE 01.09.2010
• Subjects: Circuit faults; Circuit testing; Computational modeling; Distributed diagnosis; electrical power distribution systems; hybrid bond graphs (HBGs); hybrid systems; Integrated circuit interconnections; model-based diagnosis; NASA; Power distribution; Power system modeling; Software testing; Space vehicles; System testing
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/TSMCA.2010.2052038

The application of model-based diagnosis schemes to real systems introduces many significant challenges, such as building accurate system models for heterogeneous systems with complex behaviors, dealing with noisy measurements and disturbances, and producing valuable results in a timely manner with limited information and computational resources. The Advanced Diagnostics and Prognostics Testbed (ADAPT), which was deployed at the NASA Ames Research Center, is a representative spacecraft electrical power distribution system that embodies a number of these challenges. ADAPT contains a large number of interconnected components, and a set of circuit breakers and relays that enable a number of distinct power distribution configurations. The system includes electrical dc and ac loads, mechanical subsystems (such as motors), and fluid systems (such as pumps). The system components are susceptible to different types of faults, i.e., unexpected changes in parameter values, discrete faults in switching elements, and sensor faults. This paper presents Hybrid Transcend, which is a comprehensive model-based diagnosis scheme to address these challenges. The scheme uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. The computational methods are implemented as a suite of software tools that enable diagnostic analysis and testing through simulation, diagnosability studies, and deployment on the experimental testbed. Simulation and experimental results demonstrate the effectiveness of the methodology.