Feature extraction and diagnosis of bearing fault signals based on LE and DBN algorithms
-
摘要: 为了提高机械传动系统的运行稳定性，提出了一种基于半监督拉普拉斯特征映射（semi-supervised laplacian eigenmap, SLE）和深度置信网络（deep belief network, DBN）算法的故障信号特征提取方法。选择SLE算法提取高维振动信号的流形参数，在DBN内输入流形学习数据实现特征数据二次挖掘的过程，完成不同故障的分类。研究结果表明：采用SLE-DBN模型进行处理时达到了比其余模型更优性能。采用SLE算法可以显著缩短SLE-DBN组合模型运算时间。训练集样本进行识别得到的准确率接近100%，表明模型能够对训练数据起到良好的拟合效果。SLE算法相对MCA与PCA算法表现出了更优特征提取性能，当设置合适参数时可以获得近100%的准确率。当有标签样本数量介于60~120时，DBN网络相对CNN网络表现出了更优分类性能。SLE-DBN模型对于别轴承故障诊断方面都达到了理想分类精度以及实现快速识别的要求。Abstract: In order to improve the operation stability of mechanical transmission system, a fault signal feature extraction method based on LE and DBN algorithm was proposed. LE algorithm was selected to extract manifold parameters of high-dimensional vibration signals. The manifold learning data was input into DBN to realize the secondary mining process of feature data and complete the classification of different faults. The results show that the le-DBN model used in this paper achieves better performance than other models. LE algorithm can significantly shorten the computation time of LE-DBN combined model. The recognition accuracy of training set samples is almost 100%, indicating that the model can play a good fitting effect on training data. Compared with PCA and KPCA, LE algorithm has better feature extraction performance and can achieve nearly 100% accuracy when appropriate parameters are set. When the number of label samples is between 60 and 120, DBN network shows better classification performance than CNN network. The LE-DBN model achieves the ideal classification accuracy and fast recognition requirements for different bearing fault diagnosis.
-
Key words:
- Laplacian eigenmap /
- deep confidence network /
- a semi-supervised /
- fault diagnosis
-
表 1 试验方法对比
算法对比 KPCA SLE DBN SLE-DBN 耗时/s 降维 0.13 0.38 — 0.62 预训练 — — 5.41 0.080 微调 — — 79.51 0.75 测试 0.017 0.016 0.36 0.044 总耗时 0.147 0.379 85.28 1.49 准确率/(%) 79.00 92.00 88.67 100.00 
下载: 导出CSV
表 3 多工况下试验参数设置
试验参数 数值 裂纹长度/mm 0，5，10，15 输入轴转速/(r/min) 300，600，900，1 200，1 500 负载/(N·m) 0，4 样本点数 2 000 样本维度 900
下载: 导出CSV
表 4 CNN 网络参数设置
参数 名称 大小 输出尺寸 输入层 — —— 270×1 C1 卷积核 3×1×32 270×1×32 S2 最大池化核 3×1 90×1×32 C3 卷积核 3×1×16 90×1×16 S4 最大池化核 3×1 30×1×16 FC5 — — 480×1 FC6 权值矩阵 480×100 100×1 输出层 输出层 100×4 4×1
下载: 导出CSV
表 5 不同样本数下算法的准确率
数目 60 80 100 120 CNN 85.83% 87.50% 90.50% 91.67% DBN 93.34% 94.37% 96.29% 97.11% δ −7.51% −6.87% −5.79% −5.44% 数目 140 160 180 200 CNN 95.83% 98.17% 98.87% 99.08% DBN 96.84% 98.11% 100% 100% δ −1.01% 0.06% −1.13% −0.92%
下载: 导出CSV
-
[1] El-Thalji I, Jantunen E. A summary of fault modelling and predictive health monitoring of rolling element bearings[J]. Mechanical Systems & Signal Processing, 2015, 60–61(1): 252-272. [2] Bin G F, Gao J J, Li X J, et al. Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network[J]. Mechanical Systems & Signal Processing, 2012, 27(1): 696-711. [3] 孙斌, 薛广鑫. 基于等距特征映射和支持矢量机的转子故障诊断方法[J]. 机械工程学报, 2012, 48(9): 129-135. [4] Zhang X H, Lei Y. Application of BP neural network in mechanical fault diagnosis[J]. Noise & Vibration Control, 2008, 23(23): 182-184. [5] 李巍华, 史铁林, 杨叔子. 基于核函数估计的转子故障诊断方法[J]. 机械工程学报, 2006, 42(9): 76-82. doi: 10.3321/j.issn:0577-6686.2006.09.013 [6] Jiang L, Xuan J P, Shi T L. Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis[J]. Mechanical Systems & Signal Processing, 2013, 41(1-2): 113-126. [7] Wang S, Lu J F, Gu X J, et al. Semi-supervised linear discriminant analysis for dimension reduction and classification[J]. Pattern Recognition, 2016, 57(C): 179-189. [8] Hinton G, Salakhutdinov R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647 [9] 雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104. [10] Chen Z Q, Deng S C, Chen X D, et al. Deep neural networks-based rolling bearing fault diagnosis[J]. Microelectronics Reliability, 2017, 75: 327-333. doi: 10.1016/j.microrel.2017.03.006 [11] Shao H D, Jiang H K, Zhang H Z, et al. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief Network[J]. IEEE Transactions on Industrial Electronics, 2017, 65(3): 2727-2736. [12] Liu J, Hu Y M, Wang Y, et al. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis[J]. Measurement Science & Technology, 2018, 29(5): 055103. [13] Shao H D, Jiang H Ki, Li X Q, et al. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine[J]. Knowledge-based Systems, 2018, 140: 1-14. doi: 10.1016/j.knosys.2017.10.024 [14] Hinton G. Deep belief networks[J]. Scholarpedia, 2009, 4(6): 5947. [15] 赵光权, 葛强强, 刘小勇, 等. 基于DBN 的故障特征提取及诊断方法研究[J]. 仪器仪表学报, 2016, 37(9): 1946-1953. doi: 10.3969/j.issn.0254-3087.2016.09.004 [16] 张淑清, 胡永涛, 姜安琦, 等. 基于双树复小波和深度信念网络的轴承故障诊断[J]. 中国机械工程, 2017, 28(5): 532-536. doi: 10.3969/j.issn.1004-132X.2017.05.005 -
下载:
点击查看大图
图(6) / 表(5)
计量
- 文章访问数: 171
- HTML全文浏览量: 14
- PDF下载量: 50
- 被引次数: 0
