Statistical model checking for cyber-physical systems
ATVA'11 Proceedings of the 9th international conference on Automated technology for verification and analysis
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Co-analysis of SysML and Simulink Models for Cyber-Physical Systems Design
RTCSA '12 Proceedings of the 2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Modeling and debugging numerical constraints of cyber-physical systems design
Proceedings of the Fourth Symposium on Information and Communication Technology
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This paper proposes a novel approach to do online analysis of accidental fault localization for dynamic systems by using Hidden Markov Model (HMM). By introducing reasonable and appropriate abstraction of complex system, HMM is used to represent the fault and no-fault states of system's components and system's behaviour. The HMM is parametrized to be statistically equivalent to real system's behaviour. Inspired by the principles of Fault Tree Analysis and maximum entropy in Bayesian probability theory, we propose the algorithms to estimate HMM's parameters, instead of learning, because in real systems the learning data for accidental fault is difficult to obtain. We design a specific test bed to generate large quantity of test cases, and give out the experimental results to assess the accuracy and efficiency. Meanwhile, we apply the approach to a simple helicopter control system case study, and give out convincing results.