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Sensors 2015, 15(11), 28031-28051; doi:10.3390/s151128031

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

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 19 July 2015 / Revised: 23 October 2015 / Accepted: 29 October 2015 / Published: 5 November 2015
(This article belongs to the Section Physical Sensors)
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Abstract

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. View Full-Text
Keywords: dynamic systems; fault diagnosis; concurrent probabilistic automata; Monte Carlo technique; labeled uncertainty graph dynamic systems; fault diagnosis; concurrent probabilistic automata; Monte Carlo technique; labeled uncertainty graph
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhou, G.; Feng, W.; Zhao, Q.; Zhao, H. State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph. Sensors 2015, 15, 28031-28051.

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