| 基于优化多尺度排列熵和卷积神经网络的滚动轴承故障诊断方法 |
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| 引用本文： | 伍济钢,文港.基于优化多尺度排列熵和卷积神经网络的滚动轴承故障诊断方法[J].航天器环境工程,2023,40(1):99-106. |
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| 作者姓名： | 伍济钢 文港 |
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| 作者单位： | 湖南科技大学 机械设备健康维护湖南省重点实验室，湘潭 411201 |
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| 基金项目： | 国家自然科学基金项目（编号:51775181） |
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| 摘 要： | 针对滚动轴承故障分类中特征信号微弱、信号非线性和多尺度特征难提取的问题，提出基于优化多尺度排列熵（MPE）和卷积神经网络（CNN）的滚动轴承故障诊断方法:通过改进自适应噪声完备集合经验模式分解（ICEEMDAN）对轴承信号进行分解和重构，实现信号降噪；通过粒子群算法（PSO）对MPE进行优化，提出PSO-MPE特征提取方法，参数优化后的MPE能够提取更为关键的特征信息；将所得的排列熵输入到CNN中进行故障分类以及降维可视化分析。以凯斯西储大学开放轴承数据库样本为测试对象，将文章所提出的ICEEMDAN-PSO-MPE-CNN方法与ICEEMDAN-PSO-MPE-RNN、CEEMDAN-SVM、ICEEMDAN-PSO-MPE-SVM等方法进行纵向和横向对比分析，结果表明改进方法的分类准确率和效率更高，在T-SNE可视化下的分类效果更明显，能够实现滚动轴承故障的高精度和高效率检测。
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| 关 键 词： | 滚动轴承 故障诊断 多尺度排列熵 卷积神经网络 粒子群算法 |
| 收稿时间： | 2022-10-10 |
Rolling bearing fault diagnosis method based on optimized multi-scale permutation entropy and convolutional neural network |
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| Affiliation: | Hunan Province Key Laboratory of Health Maintenance Equipment, Hunan University of Science and Technology, Xiangtan 411201, China |
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| Abstract: | In view of weak feature signal, signal nonlinearity and difficult extraction of multi-scale features in rolling bearing fault classification, a rolling bearing fault diagnosis method based on optimized multi-scale permutation entropy (MPE) and convolutional neural network (CNN) was proposed. The decomposition and reconstruction of bearing signals by improving complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) could achieve the signal noise reduction. The MPE was optimized by particle swarm optimization (PSO), the PSO-MPE feature extraction method was proposed, and the parameter-optimized MPE could extract more critical feature information. The resulted permutation entropy was input into CNN for fault classification and dimensionality reduction visualization analysis. The ICEEMDAN-PSO-MPE-CNN method proposed in this paper was analyzed and compared with CEEMDAN-SVM, ICEEMDAN-PSO-MPE-RNN, ICEEMDAN-PSO-MPE-SVM and other methods longitudinally and cross-sectionally by taking Case Western Reserve University’s open bearing database samples as test objects. The results show that the improved method enjoys higher classification accuracy and efficiency with more obvious classification effect under T-SNE visualization, thus can realize the high precision and high efficiency detection of rolling bearing faults. |
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