Behavioral-Based Monitoring of Networked Systems With Application to Spacecraft Li-Ion Battery Health Management

Performance monitoring is essential for the safety maintenance and health management of networked systems. In this work, we propose a behavioral-based monitoring approach for networked systems, which directly utilizes input and output measurements without parametric identification. A regularized cost function and system dynamic constraints are designed to reduce the disturbance of system nonlinearity and noise, which can be used to predict future trajectories of networked systems. Missing data during the long-distance transmission are also estimated through the fundamental lemma in behavioral system theory. Then, a statistical inference strategy based on the difference between the predicted trajectories and the real measurement trajectories is introduced to detect system faults. The effectiveness of the proposed approach is illustrated through experimental applications to the spacecraft Li-ion battery health management, which includes charging and discharging trajectory prediction, missing data estimation, and voltage sensor and overdischarge fault detection.