| Citation: | LIU Jiufu, ZHANG Xinzhe, WANG Hengyu, et al. Partial observable Petri nets fault diagnosis with quantum Bayesian learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1125-1134. doi: 10.13700/j.bh.1001-5965.2021.0010(in Chinese)
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This paper proposes an algorithm to construct a quantum Bayesian Petri nets model for the fault, and uses the sub net model to analyze the fault of Petri net system. According to the reachability identification diagram, which is the transition firing path can not judge the system state, establish the quantum Bayesian Petri nets subnet model to tackle the unobservable faults in partial observable Petri model. Through the quantum interference caused by the uncertain path, recalibrate the conditional probability table of the transition to obtain the quantum probability amplitude table. According to the pre-set of fault transition and quantum Bayesian reasoning, calculates the firing prior probability of transition. The posterior probability is modified by the observable transition in the post-set, and the state of the system is estimated by the maximum posterior probability. When the fault transition is not unique, the fault with the maximum probability is selected as the fault source. Finally, establishes a partial observable Petri nets model of a real fault system. Combined with the probability sequence information of observable label and quantum Bayesian probability estimation, the fault diagnosis of the unobservable parts of the system is carried out to verify the effectiveness of the algorithm with the data in simulation experiment.
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