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.

I. Introduction

Networked systems are highly reliable and flexibly configured systems with spatially distributed components transmitting data over communication networks [1], [2], [3], and have been adopted in a wide range of situations [4], [5], [6]. System reliability and safety are essential to the whole life health management and the long term safety maintenance of networked systems. However, the measurement data could be delayed and lost due to energy and bandwidth constraints [7], [8]. In response to the limited transmission and complex structure of networked systems, intelligent performance monitoring approaches have received extensive attention in recent years [9], [10], [11].