基于特征选择的集成极限学习机故障辨识方法

兰州理工大学学报 ›› 2025, Vol. 51 ›› Issue (2): 44-50.

基于特征选择的集成极限学习机故障辨识方法

马驰¹, 赵荣珍^*1,2, 原健辉¹, 郑玉巧¹

1.兰州理工大学机电工程学院, 甘肃兰州 730050;
2.广州理工学院智能制造与电气工程学院, 广东广州 510540

收稿日期:2022-11-25 出版日期:2025-04-28 发布日期:2025-04-29
通讯作者: 赵荣珍(1960-),女,山东枣庄人,博士,教授,博导.Email:zhaorongzhen@lut.edu.cn
基金资助:
国家自然科学基金(52465012),甘肃省优秀研究生“创新之星”项目(2022CXZX-415)

Research on fault identification method of ensemble extreme learning machine based on feature selection

MA Chi¹, ZHAO Rong-zhen^1,2, YUAN Jian-hui¹, ZHENG Yu-qiao¹

1. School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
2. School of Intelligent Manufacturing and Electrcal Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China

Received:2022-11-25 Online:2025-04-28 Published:2025-04-29

摘要/Abstract

摘要： 针对传统极限学习机神经网络在处理复杂数据时无法获得最佳分类性能的问题,提出了基于特征选择的集成极限学习机故障辨识方法.首先,选择合适的尺度对振动信号进行粗粒化分解,在不同尺度上计算振动信号的模糊近似熵,并构成高维数据集.然后,通过邻域粗糙集算法对高维数据集进行属性约简,并且采用不同的邻域半径对数据集进行约简,从而产生不同的特征子集,同时将每个特征子集划分为训练集和测试集,进而输入极限学习机进行模式识别.最后,整合多个极限学习机的预测结果,依据相对多数投票法决定最终的辨识结果.实验证明,相比传统极限学习机,该方法可以提高滚动轴承故障类别的辨识精度,使故障分类结果更准确、更有效.

关键词: 模糊近似熵, 特征选择, 分类器集成, 极限学习机

Abstract: Aiming at the problem that the traditional extreme learning machine neural network cannot get the best classification performance when processing complex data, an improved method of integrated extreme learning machine based on feature selection is proposed. And it is used to calculate to constitute a high-dimensional data set. Then the high-dimensional data set is attribute reduced by the neighborhood rough set algorithm, and different feature subsets are generated by using different neighborhood radii to reduce the data setach feature subset is divided into training set and test set and input to the extreme learning machine for pattern recognition. Finally, the prediction results multiple extreme learning machines are integratedfinal recognition results the relative majority voting method. It is proved thatcomparing with traditional extreme learning machine this method can improve the recognition accuracy of rolling bearing fault categories and make the fault classification results more accurate and effective.

Key words: fuzzy approximate entropy, feature selection, classifier ensemble, extreme learning machine

中图分类号:

TH133
TH165

马驰, 赵荣珍, 原健辉, 郑玉巧. 基于特征选择的集成极限学习机故障辨识方法[J]. 兰州理工大学学报, 2025, 51(2): 44-50.

MA Chi, ZHAO Rong-zhen, YUAN Jian-hui, ZHENG Yu-qiao. Research on fault identification method of ensemble extreme learning machine based on feature selection[J]. Journal of Lanzhou University of Technology, 2025, 51(2): 44-50.

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