| Citation: | WANG J H，TANG G D，CAO J，et al. Fault diagnosis method of BN ball mill rolling bearing based on AESL-GA[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（4）：1138-1146 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0428
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To address the imperfect and imprecise shortcomings of the knowledge-based Bayesian network (BN) construction method, this paper proposes a BN structure construction method based on knowledge guidance and data mining. Firstly, aiming at the problem of inaccurate fault diagnosis results of a single signal and the uncertainty in fault information, the current signal and the vibration signal are fused to establish the characteristic nodes of the BN. The fault characteristic parameters of the two kinds of signals are extracted respectively, and the feature selection is carried out by the distinguish index method, which is used as the node of the feature layer of the BN structure. Secondly, the initial BN structure constructed by expert knowledge is combined with the adaptive elite-based structure learner using genetic algorithm (AESL-GA) to optimize the structure. By adaptively restricting the search space in the evolution process, reducing the number of free parameters and improving its global search ability, we obtain optimal BN structure. Finally, the method is verified by the measured data of the ball mill rolling bearing of Jinchuan Company and the data set of Paderborn University, which proves the effectiveness of the proposed method.
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