基于多层置信规则库的钢疲劳预测方法
A Fatigue Prediction Method for Steel Based on the Multi-Layer Belief Rule Base
DOI: 10.12677/airr.2024.134080, PDF,    科研立项经费支持
作者: 蒋毅行, 付 伟*, 马 宁：哈尔滨师范大学计算机科学与信息工程学院，黑龙江 哈尔滨
关键词: 钢疲劳预测；置信规则库；参数优化；证据推理；Steel Fatigue Prediction； Confidence Rule Base； Parameter Optimization； Evidentiary Reasoning
摘要: 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。
Abstract: 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.
文章引用：蒋毅行, 付伟, 马宁. 基于多层置信规则库的钢疲劳预测方法[J]. 人工智能与机器人研究, 2024, 13(4): 780-787. https://doi.org/10.12677/airr.2024.134080

参考文献

[1] Fajri, A., Prabowo, A.R., Muhayat, N., Smaradhana, D.F. and Bahatmaka, A. (2021) Fatigue Analysis of Engineering Structures: State of Development and Achievement. Procedia Structural Integrity, 33, 19-26. [Google Scholar] [CrossRef] 
[2] Agrawal, A., Deshpande, P.D., Cecen, A., Basavarsu, G.P., Choudhary, A.N. and Kalidindi, S.R. (2014) Exploration of Data Science Techniques to Predict Fatigue Strength of Steel from Composition and Processing Parameters. Integrating Materials and Manufacturing Innovation, 3, 90-108. [Google Scholar] [CrossRef] 
[3] 张旭泽. 基于机器学习的金属材料超高周疲劳寿命预测[D]: [硕士学位论文]. 武汉: 武汉纺织大学, 2023.
[4] 申建国, 汪舟, 卢伟, 等. 有限元模拟和神经网络相结合的喷丸处理SAE9254钢疲劳寿命预测[J]. 机械工程材料, 2024, 48(7): 77-84.
[5] Agrawal, A., Deshpande, P.D., Cecen, A., Basavarsu, G.P., Choudhary, A.N. and Kalidindi, S.R. (2014) Exploration of Data Science Techniques to Predict Fatigue Strength of Steel from Composition and Processing Parameters. Integrating Materials and Manufacturing Innovation, 3, 90-108. [Google Scholar] [CrossRef] 
[6]  Yu, X. (2016) Material Parameter Identification of the Stamping Forming Plate of Advanced High Strength Steel Based on Bayesian Inference. M.Sc. Thesis, Hunan University.
[7] Zhang, M., Sun, C., Zhang, X., Goh, P.C., Wei, J., Hardacre, D., et al. (2019) High Cycle Fatigue Life Prediction of Laser Additive Manufactured Stainless Steel: A Machine Learning Approach. International Journal of Fatigue, 128, Article ID: 105194. [Google Scholar] [CrossRef] 
[8] Hu, D., Su, X., Liu, X., Mao, J., Shan, X. and Wang, R. (2020) Bayesian-Based Probabilistic Fatigue Crack Growth Evaluation Combined with Machine-Learning-Assisted GPR. Engineering Fracture Mechanics, 229, Article ID: 106933. [Google Scholar] [CrossRef] 
[9] Amiri, N., Farrahi, G.H., Kashyzadeh, K.R. and Chizari, M. (2020) Applications of Ultrasonic Testing and Machine Learning Methods to Predict the Static & Fatigue Behavior of Spot-Welded Joints. Journal of Manufacturing Processes, 52, 26-34. [Google Scholar] [CrossRef] 
[10] Yang, Y.L., Wang, L. and Ma, C.H. (2020) Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization. Laser & Optoelectronics Progress, 57, Article ID: 053002. [Google Scholar] [CrossRef] 
[11] Yang, J.-B., Liu, J., Wang, J., Sii, H.-S. and Wang, H.-W. (2006) Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach-Rimer. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 36, 266-285. [Google Scholar] [CrossRef] 
[12] Cao, Y., Zhou, Z., Tang, S., Ning, P. and Chen, M. (2023) On the Robustness of Belief-Rule-Based Expert Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53, 6043-6055. [Google Scholar] [CrossRef] 
[13] Cao, Y., Zhou, Z., Hu, C., He, W. and Tang, S. (2021) On the Interpretability of Belief Rule-Based Expert Systems. IEEE Transactions on Fuzzy Systems, 29, 3489-3503. [Google Scholar] [CrossRef] 
[14] 周志杰, 曹友, 胡昌华, 等. 基于规则的建模方法的可解释性及其发展[J]. 自动化学报, 2021, 47(6): 1201-1216.
[15] 程晓玉, 韩鹏, 贺维, 等. 一种新的基于可解释性置信规则库的飞轮健康状态评估模型[J]. 航空学报, 2023, 44(S1): 177-189.
[16] 董昕昊, 周志杰, 胡昌华, 等. 基于分层置信规则库的惯导系统性能评估方法[J]. 航空学报, 2021, 42(7): 441-451.
[17] 朱海龙, 耿文强, 韩劲松, 等. 利用置信规则库构建WSN节点故障检测模型[J]. 智能系统学报, 2021, 16(3): 511-517.
[18] Frie, C., Riza Durmaz, A. and Eberl, C. (2024) Exploration of Materials Fatigue Influence Factors Using Interpretable Machine Learning. Fatigue & Fracture of Engineering Materials & Structures, 47, 2752-2773. [Google Scholar] [CrossRef] 
[19] Chen, Y., Yang, J., Xu, D., Zhou, Z. and Tang, D. (2011) Inference Analysis and Adaptive Training for Belief Rule Based Systems. Expert Systems with Applications, 38, 12845-12860. [Google Scholar] [CrossRef] 
[20] Yang, J. and Xu, D. (2013) Evidential Reasoning Rule for Evidence Combination. Artificial Intelligence, 205, 1-29. [Google Scholar] [CrossRef] 
[21] Hu, G., Zhou, Z., Zhang, B., Yin, X., Gao, Z. and Zhou, Z. (2016) A Method for Predicting the Network Security Situation Based on Hidden BRB Model and Revised CMA-ES Algorithm. Applied Soft Computing, 48, 404-418. [Google Scholar] [CrossRef] 

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