国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理
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| 标 题： |
A Machine Learning Framework Based on ACO-EMD-CKAN for Bearing Fault Detection
基于ACO-EMD-CKAN的机器学习框架用于轴承故障检测 |
| 作 者： |
Qingqing Zhou |
| 关键词： |
Convolution kolmogorov-Arnold network [卷积高斯-阿诺德网络]
Empirical mode decomposition [ 经验模态分解]
Ant colony optimization [ 蚁群优化]
Fault diagnosis [ 故障诊断]
Rolling bearing [ 滚动轴承] |
| 出版年： |
2024 |
| 文集名： |
6th IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT 2024)
第六届IEEE国际民用航空安全与信息技术会议（ICCASIT 2024） |
| 会议名： |
6th IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT 2024)
第六届IEEE国际民用航空安全与信息技术会议（ICCASIT 2024） |
| 届 次： |
6th |
| 摘 要： |
Rolling bearings are the core components of rotating machinery, so their health condition is directly related to safe production. However, there are still some problems in the traditional fault diagnosis of rolling bearings with low accuracy, which is difficult to adaptively extract typical traits. Therefore, a new method for motor rolling bearing fault diagnosis based on ACO-EMD-CKAN is proposed in this work. Firstly, the data is optimized by Ant Colony Optimization (ACO) to determine the optimal number of Intrinsic Mode Function (IMF) values. Then, denoising is performed through Empirical Mode Decomposition (EMD), and more prominent feature vectors are extracted. Next, these vectors are sent to the Convolution Kolmogorov-Arnold Network (CKAN). After passing through three layers of convolution and three layers of maximum pooling, they are finally sent to the Kolmogorov-Arnold Network (KAN) layer to perform adaptive feature extraction of the signal. Compared with the common bearing fault diagnosis methods, the proposed ACO-EMD-CKAN model has the highest accuracy, reaching 98.88%. The experimental results verify the feasibility of the model for effective classification of bearing faults, and provide important application value for efficient and reliable bearing fault detection in the future.
滚动轴承是旋转机械的核心部件，其健康状况直接关系到安全生产。然而，传统滚动轴承故障诊断方法在精度上仍存在不足，难以自适应地提取典型特征。因此，本文提出一种基于ACO-EMD-CKAN的电机滚动轴承故障诊断新方法。首先，采用蚁群优化算法（ACO）对数据进行优化，以确定本征模函数（IMF）的最佳个数。然后，通过经验模态分解（EMD）进行降噪处理，提取出更具代表性的特征向量。接着，将这些向量输入卷积科莫戈罗夫-阿诺德网络（CKAN）。经过三层卷积和三层最大池化后，最终送入科莫戈罗夫-阿诺德网络（KAN）层，实现信号的自适应特征提取。与现有常见的轴承故障诊断方法相比，所提出的ACO-EMD-CKAN模型具有最高的准确率，达到98.88%。实验结果验证了该模型在有效分类轴承故障方面的可行性，为未来高效、可靠地检测轴承故障提供了重要应用价值。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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