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基于多层置信规则库的钢疲劳预测方法
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Abstract:
钢的疲劳断裂是工业中最常发生的灾难之一。为有效预测钢疲劳，提出了一种基于多层置信规则库(Multilayer Belief Rule Base, MBRB)预测模型。首先，采用主成分分析对关键特征进行筛选。其次，利用具有可解释性约束的投影协方差矩阵自适应进化策略(P-CMA-ES)对模型参数进行优化，以提高模型的精度。最后，以美国国家材料科学研究所(NIMS) MatNavi的钢疲劳数据集为例进行了钢疲劳预测，验证了该模型的有效性，同时多层BRB解决了传统BRB组合规则爆炸的问题。与其他方法相比，该模型具有更高的精度与透明的推理过程。
Fatigue fracture of steel is one of the most common disasters in industry. In order to effectively predict steel fatigue, a prediction model based on Multilayer Belief Rule Base (MBRB) was proposed. Firstly, principal component analysis was used to screen the key features. Secondly, the projection covariance matrix adaptive evolution strategy with interpretability constraints (P-CMA-ES) was used to optimize the parameters of the model to improve the accuracy of the model. Finally, the steel fatigue dataset of MatNavi of the National Institute of Materials Science (NIMS) of United States is used as an example to predict the effectiveness of the model, and the multi-layer BRB solves the problem of the explosion of traditional BRB combination rules. Compared with other methods, the model has higher accuracy and transparent inference process.
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