State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph

Sensors

Gan Zhou²  Wenquan Feng²  Qi Zhao¹  Hongbo Zhao² 

[1] School of Electronic and Information Engineering, Beihang University, Beijing 100191, China;

关键词: dynamic systems;    fault diagnosis;    concurrent probabilistic automata;    Monte Carlo technique;    labeled uncertainty graph;   

DOI  :  10.3390/s151128031

来源: mdpi

【 摘 要 】

Cyber-physical systems such as autonomous spacecraft, power plants and automotive systems become more vulnerable to unanticipated failures as their complexity increases. Accurate tracking of system dynamics and fault diagnosis are essential. This paper presents an efficient state estimation method for dynamic systems modeled as concurrent probabilistic automata. First, the Labeled Uncertainty Graph (LUG) method in the planning domain is introduced to describe the state tracking and fault diagnosis processes. Because the system model is probabilistic, the Monte Carlo technique is employed to sample the probability distribution of belief states. In addition, to address the sample impoverishment problem, an innovative look-ahead technique is proposed to recursively generate most likely belief states without exhaustively checking all possible successor modes. The overall algorithms incorporate two major steps: a roll-forward process that estimates system state and identifies faults, and a roll-backward process that analyzes possible system trajectories once the faults have been detected. We demonstrate the effectiveness of this approach by applying it to a real world domain: the power supply control unit of a spacecraft.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.