基于重构的半监督ELM及其在故障诊断中的应用

doi:10.11949/j.issn.0438-1157.20161252

化工学报 ›› 2017, Vol. 68 ›› Issue (6): 2447-2454.DOI: 10.11949/j.issn.0438-1157.20161252

基于重构的半监督ELM及其在故障诊断中的应用

易维淋, 田学民, 张汉元

中国石油大学(华东)信息与控制工程学院, 山东青岛 266580

收稿日期:2016-09-06 修回日期:2017-02-06 出版日期:2017-06-05 发布日期:2017-06-05
通讯作者: 田学民
基金资助:
国家自然科学基金项目（61273160）

Reconstruction based semi-supervised ELM and its application in fault diagnosis

YI Weilin, TIAN Xuemin, ZHANG Hanyuan

College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, Shandong, China

Received:2016-09-06 Revised:2017-02-06 Online:2017-06-05 Published:2017-06-05
Contact: 10.11949/j.issn.0438-1157.20161252
Supported by:
supported by the National Natural Science Foundation of China (61273160)

摘要/Abstract

摘要：

工业过程中获取带标签的故障数据困难，而无标签故障数据却大量存在，如何有效地利用数据信息进行故障诊断是故障诊断领域的重要内容。为更充分地挖掘和利用数据信息，提出一种新的半监督学习方法：基于重构的半监督极限学习机（RSELM）。相比于传统的半监督极限学习机（ELM）方法，RSELM采用自动编码ELM（ELM-AE）获得的输出权重替代随机的隐含层输入权重，能更有效地提取数据特征；考虑到数据均可由其近邻数据来线性重构，故可构建近邻数自适应选择的重构图，并同时利用数据的标签信息优化连接权重，以更优地反映数据结构信息；通过建立新的含局部保持的目标函数，可有效地训练分类器。标准数据集和TE过程上的仿真实验验证了所提算法的有效性。

关键词: 半监督极限学习机, 重构, ELM-AE, 故障诊断

Abstract:

It is difficult to obtain labeled fault data while there are a multitude of unlabeled data available in industrial process, so how to utilize data information effectively is an important focal point in the field of fault diagnosis. A new semi-supervised learning method, reconstruction-based semi-supervised extreme learning machine (RSELM), was proposed for more sufficient data mining and information usage. Compared to traditional semi-supervised ELM, RSELM replaced random input weight in hidden layer with output weight, which was obtained by ELM auto-encoder (ELM-AE), such that data feature was extracterd more effectively. Since data could be reconstructed linearly by its neighbors, a self-adaptive reconstruction graph of neighboring data in combination with connection weight of optimal labeled data better reflected data structure information. A novel objective function preserving local structure information was further built to train classifier effectively. Simulation experiment on standard datasets and TE process demonstrated effectiveness of the proposed algorithm.

Key words: semi-supervised ELM, reconstruction, ELM-AE, fault diagnosis

中图分类号:

TP277

易维淋, 田学民, 张汉元. 基于重构的半监督ELM及其在故障诊断中的应用[J]. 化工学报, 2017, 68(6): 2447-2454.

YI Weilin, TIAN Xuemin, ZHANG Hanyuan. Reconstruction based semi-supervised ELM and its application in fault diagnosis[J]. CIESC Journal, 2017, 68(6): 2447-2454.

参考文献 31

[1]	GE Z, SONG Z, GAO F. Review of recent research on data-based process monitoring[J]. Industrial & Engineering Chemistry Research, 2013, 52(10): 3543-3562.
[2]	QIN S J. Survey on data-driven industrial process monitoring and diagnosis[J]. Annual Reviews in Control, 2012, 36(2): 220-234.
[3]	ZHANG Y, MA C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS[J]. Chemical Engineering Science, 2011, 66(1): 64-72.
[4]	刘强, 柴天佑, 秦泗钊, 等. 基于数据和知识的工业过程监视及故障诊断综述[J]. 控制与决策, 2010, 25(6): 801-807.
	LIU Q, CHAI T Y, QIN S Z, et al. Progress of data-driven and knowledge-driven process monitoring and fault diagnosis for industry process[J]. Control & Decision, 2010, 25(6): 801-807.
[5]	李晗, 萧德云. 基于数据驱动的故障诊断方法综述[J]. 控制与决策, 2011, 26(1): 1-9.
	LI H, XIAO D Y. Survey on data driven fault diagnosis methods[J]. Control & Decision, 2011, 26(1): 1-9.
[6]	HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: a new learning scheme of feedforward neural networks[C]//IEEE International Joint Conference on Neural Networks. Proceedings. IEEE Xplore, 2004, 2: 985-990.
[7]	HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501.
[8]	HUANG G B, WANG D H, LAN Y. Extreme learning machines: a survey[J]. International Journal of Machine Learning & Cybernetics, 2011, 2(2): 107-122.
[9]	GAO H, HUANG G B, SONG S, et al. Trends in extreme learning machines: a review[J]. Neural Networks the Official Journal of the International Neural Network Society, 2015, 61: 32.
[10]	HUANG G B. An insight into extreme learning machines: random neurons, random features and kernels[J]. Cognitive Computation, 2014, 6(3): 376-390.
[11]	HUANG G B, CHEN L, SIEW C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes[J]. IEEE Transactions on Neural Networks, 2006, 17(4): 879-892.
[12]	HUANG G B, CHEN L. Enhanced random search based incremental extreme learning machine[J]. Neurocomputing, 2008, 71(16/17/18): 3460-3468.
[13]	HUANG G B, DING X, ZHOU H. Optimization method based extreme learning machine for classification[J]. Neurocomputing, 2010, 74(1/2/3): 155-163.
[14]	HUANG G B, ZHOU H, DING X, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B(Cybernetics), 2012, 42(42): 513-529.
[15]	HUANG G B. An insight into extreme learning machines: random neurons, random features and kernels[J]. Cognitive Computation, 2014, 6(3): 376-390.
[16]	CAMBRIA E, HUANG G B, KASUN L L C, et al. Extreme learning machines[J]. Intelligent Systems, IEEE, 2013, 28(6): 30-59.
[17]	ZONG W, HUANG G B, CHEN Y. Weighted extreme learning machine for imbalance learning[J]. Neurocomputing, 2013, 101(3): 229-242.
[18]	KASUN L L C, ZHOU H, HUANG G B, et al. Representational learning with ELMs for big data[J]. Intelligent Systems IEEE, 2013, 28(6): 31-34.
[19]	MARTINEZ-REGO D, FONTENLA-ROMERO O, PEREZ-SANCHEZ B, et al. Fault Prognosis of Mechanical Components Using On-Line Learning Neural Networks[M]//Artificial Neural Networks-ICANN 2010. Berlin , Heidelberg: Springer, 2010: 60-66.
[20]	MUHAMMAD I G, TEPE K E, ABDEL-RAHEEM E. QAM equalization and symbol detection in OFDM systems using extreme learning machine[J]. Neural Computing and Applications, 2013, 22(3): 491-500.
[21]	WANG C, WEN C, LU Y. A fault diagnosis method by using extreme learning machine[C]//International Conference on Estimation, Detection and Information Fusion. IEEE, 2015: 318-322.
[22]	LIU J, CHEN Y, LIU M, et al. SELM: Semi-supervised ELM with application in sparse calibrated location estimation[J]. Neurocomputing, 2011, 74(16): 2566-2572.
[23]	IOSIFIDIS A, TEFAS A, PITAS I. Regularized extreme learning machine for multi-view semi-supervised action recognition[J]. Neurocomputing, 2014, 145(18): 250-262.
[24]	HUANG G, SONG S, GUPTA J N, et al. Semi-supervised and unsupervised extreme learning machines[J]. IEEE Transactions on Cybernetics, 2014, 44(12): 2405-2417.
[25]	ZHOU Y, LIU B, XIA S, et al. Semi-supervised extreme learning machine with manifold and pairwise constraints regularization[J]. Neurocomputing, 2015, 149(PA): 180-186.
[26]	AVERDI B, MARQES I, GRANA M. Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation[J]. Neurocomputing, 2015, 149: 373-386.
[27]	ROWEIS S T, SAUAL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.
[28]	ZHAO H. Combining labeled and unlabeled data with graph embedding[J]. Neurocomputing, 2006, 69(16/17/18): 2385-2389.
[29]	SINDHWANI V, NIYOGI P, BELKIN M. Beyond the point cloud: from transductive to semi-supervised learning[C]//ICML'05 Proceedings of the 22nd International Conference on Machine Learning Bonn, Germany, 2005: 824-831.
[30]	MELACCI S, BELKIN M. Laplacian support vector machines trained in the primal[J]. Journal of Machine Learning Research, 2009, 12(5): 1149-1184.
[31]	LEE J M, QIN S J, LEE I B. Fault detection of non-linear processes using kernel independent component analysis[J]. Canadian Journal of Chemical Engineering, 2008, 85(4): 526-536.

基于重构的半监督ELM及其在故障诊断中的应用

Reconstruction based semi-supervised ELM and its application in fault diagnosis

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