近年来，基于稀疏表示的分类技术在模式识别中取得一定的成功。该框架中，字典的学习和分类器的训练通常是两个独立的模块，降低了方法的识别精度。针对以上问题，提出了一种特征提取和模式识别相融合的改进判别字典学习模型，将重构误差项、稀疏编码判别项及分类误差项进行了整合，并用K奇异值分解算法对目标函数进行优化，实现了字典和分类器的同步学习。该方法先对原始信号进行经验模态分解，并从分解的本征模态函数中提取时、频特征，形成故障样本；然后将训练样本输入改进模型用K奇异值分解优化；最后用习得字典及分类器权重对测试样本进行识别。实验结果表明：该算法不但适用于小样本故障问题，而且鲁棒性和分类性能都明显高于其它算法。
Abstract
In recent years, sparse representation based classification method has been successfully employed in pattern recognition. The learning of dictionary and the training of classifier in this method are usually independent in existing approaches, so it reduces the identification accuracy. In this paper, we propose a novel fault diagnosis method based on improved dictionary learning model, which integrates discriminative sparse coding error and classification performance criterion with the reconstruction error. And this model is solved by K-singular value decomposition (K-SVD) algorithm that realizes the synchronization learning of dictionary and classifier. For our method, original signal is decomposed firstly by empirical mode decomposition, and the features of time domain and frequency domain are extracted from the decomposed intrinsic mode functions; then training samples are input into the improved model that is optimized by K-SVD; finally testing samples are identified by using learned dictionary and classification weights. Experimental results show that the algorithm not only can be applied in the small sample faults diagnosis, and the robustness and classification performance are significantly higher than other algorithms.
关键词
稀疏编码 /
字典学习 /
经验模态分解 /
故障诊断
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Key words
spare coding /
dictionary learning /
empirical mode decomposition /
fault diagnosis
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参考文献
[1] Smith, E.C. and Lewicki, M.S. Efficient auditory coding[J]. Nature, 2006, 439(7079): 978-982.
[2] Liu, H., Liu, C. and Huang, Y. Adaptive feature extraction using sparse coding for machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 558-574.
[3] Chakraborty S, Chatterjee A, Goswami S K. A sparse representation based approach for recognition of power system transients[J]. Engineering Applications of Artificial Intelligence, 2014,30:137-144.
[4] Wright, J., Yang, A.Y., Ganesh, A., et al. Robust face recognition via sparse representation[J]. Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[5] Zhang Q, Li B X. Discriminant K-SVD for dictionary learning in face recognition[C]. In: Proc CVPR. 2010: 2691-2698.
[6] Yang J C, Yu K, Gong Y, et al. Linear spatial pyramid matching using sparse coding for image classification[C]. In: Proc CVPR. 2009:1794-1801.
[7] J. Winn, A. Criminisi, and T. Minka. Object Categorization by Learned Universal Visual Dictionary[C]. In: Proc. IEEE Int’l Conf. Computer Vision, 2005.
[8] M. Yang, L. Zhang, X. Feng, and D. Zhang. Fisher Discrimination Dictionary Learning for Sparse Representation[C]. In: Proc. IEEE Int’l Conf. Computer Vision, 2011.
[9] J. Yang, K. Yu, and T. Huang. Supervised Translation-Invariant Sparse Coding[C]. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[10] Aharon, M., Elad, M. and Bruckstein, A. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation[J]. Signal Processing, 2006, 54(11): 4311-4322.
[11] Tropp, J.A. and Gilbert, A.C. Signal recovery random measurements via orthogonal matching pursuit[J]. Information Theory, 2007, 53(12): 4655-4666.
[12] N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]. Proc. Roy. Soc. Lond. Ser., 1998, 903-995.
[13] 赵协广. 基于小波变换和经验模态分解的滚动轴承故障诊断方法研究[D]. 山东科技大学, 2009.
Xieguang Zhao. Study of the fault diagnosis methods of rolling bearing based on wavelet transform and empirical mode decomposition[D]. Shandong University of Science and Technology, 2009.
[14] Y.G. Lei, Z.J. He, Y.Y. Zi, A new approach to intelligent fault diagnosis of rotating machinery[J]. Exp. Syst. Appl., 2008, 35: 1593-1600.
[15] Meng Yang, Lei Zhang, Jian Yang, and David Zhang. Robust Sparse Coding for Face Recognition[C]. In: IEEE Conference on Computer Vision and Pattern Recognition, 2011: 625-632.
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