Paper Titles

A Simaplied Adaptive Kalman Filtering Algorithm
p.378

A Scalable D Dominant Strategy to Solve Large-Dimensional Multi-Objective Optimization Problems
p.383

Using the Improved Genetic BP Neural Network Algorithm for Lithologic Identification
p.389

Applications of Color Morphology in Image Denoising
p.393

A Hierarchical Feature Selection Method Based on Classification Tree for HGU Fault Diagnosis
p.398

BP Neural Network Based Optimization for China Railway Freight Transport Network
p.404

A Novel Extracting Algorithm of the Low Gray Fuzzy Edges for Infrared Images
p.411

A Fast Target Searching and Capturing Method for Phased Array Radar
p.416

Parameters Selection of LLE Algorithm for Classification Tasks
p.422

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1037A Hierarchical Feature Selection Method Based on...

A Hierarchical Feature Selection Method Based on Classification Tree for HGU Fault Diagnosis

Article Preview

Abstract:

Fault diagnosis is very important to ensure the safe operation of hydraulic generator units (HGU). Because of the complexity of HGU, the vast amounts of measured data and the redundant information, the accuracy and instantaneity of fault diagnosis are severely limited. At present, feature selection technique is an effective method to break through this bottleneck. According to the specific characteristics of HGU faults, this paper puts forward a hierarchical feature selection method based on classification tree (HFSMCT). HFSMCT selects the most effective feature for each branch node through filtering evaluation criteria and heuristic search strategy, and all the selected features constitute the final feature set. Moreover, HFSMCT is easy to design and implement, and it is very prominent in computational efficiency and accuracy. The simulation results also prove that HFSMCT is very suitable for HGU fault diagnosis.

You might also be interested in these eBooks

Development of Applied Engineering Sciences

Info:

Periodical:

Advanced Materials Research (Volume 1037)

Pages:

398-403

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1037.398

Citation:

Cite this paper

Online since:

October 2014

Authors:

Xiao Yue Chen*, Jian Zhong Zhou, Xiao Min Xu, Yong Chuan Zhang

Keywords:

Fault Diagnosis, Feature Selection, Filtering Evaluation Criteria, Heuristic Search Strategy, Hydraulic Generator Unit

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] X. Zhang, J. Zhou, J. Guo, Q. Zou, Z. Huang, Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine, Expert. Syst. Appl. 39 (2012) 2621-2628.

DOI: 10.1016/j.eswa.2011.08.117

[2] X. Chen, J. Zhou, H. Xiao, E. Wang, J. Xiao & H. Zhang, Fault diagnosis based on comprehensive geometric characteristic and probability neural network. Applied Mathematics and Computation, 230(2014) 542-554.

DOI: 10.1016/j.amc.2013.12.122

[3] Bing, P., et al., Hydropower Generating Unit's Fault Diagnosis Based on Information Fusion. AUTOMATION OF ELECTRIC POWER SYSTEMS, 2008. 32(13): pp.76-80.

[4] M. Zhou, J. Zhou & Z. Liu, Support Vector Machines Based Approach for Hydroelectric Generating Unit Fault Diagnosis. CHINA RURAL WATER AND HYDROPOWER, 2008(1): pp.114-116, 119.

[5] Wuke, L., et al., Fault diagnosis of hydroelectric units based on rough set & RBF network. CHINESE JOURNAL OF SCIENTIFIC INSTRUMENT, 2007. 28(10): pp.1806-1810.

[6] Bin, H. and Z. Jian-zhong, The data fusion algorithm for hydropower generater unit fault diagnosis. HYDROPOWER AUTOMATION AND DAM MONITORING, 2004. 28(1): pp.12-15.

[7] Zhiwei, J.I., H.U. Min and Y. Jian-xin, A survey of feature selection algorithm. ELECTRONIC DESIGN ENGINEERING, 2011. 19(9): pp.46-51.

[8] Yong, M., et al., A Survey for Study of Feature Selection Algorithms. PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2007. 20(2): pp.211-218.

[9] Xu, Y, et al., Summary of feature selection algorithms. Control and Decision, 2012. 27(2): pp.161-166, 192.

[10] Juan, W., C.I. Lin-lin and Y. Kang-ze, A Survey of Feature Selection. COMPUTER ENGINEERING AND SCIENCE, 2005. 27(12): pp.68-71.

[11] Sun, Z., G. Bebis and R. Miller, Object detection using feature subset selection. Pattern Recognition, 2004. 37(11): pp.2165-2176.

DOI: 10.1016/j.patcog.2004.03.013

[12] Narendra, P.M. and K. Fukunaga, A branch and bound algorithm for feature subset selection. 1977. C-26(9): pp.917-22.

DOI: 10.1109/tc.1977.1674939

[13] Nakariyakul, S. and D.P. Casasent, Adaptive branch and bound algorithm for selecting optimal features. Pattern Recognition Letters, 2007. 28(12): pp.1415-1427.

DOI: 10.1016/j.patrec.2007.02.015

[14] Tsymbal, A. and S. Puuronen. Ensemble feature selection with the simple Bayesian classification in medical diagnostics. in Computer-Based Medical Systems, 2002. (CBMS 2002). Proceedings of the 15th IEEE Symposium on. (2002).

DOI: 10.1109/cbms.2002.1011381

[15] Wu B L, Tom A, David F, et al. Comparison of statistical methods for classiﬁcation of ovarian cancer using mass spectrometry data[J]. Bioinfor-matics, 2003, 19(13): 1636-1643.

[16] Furlanello, C., et al., An accelerated procedure for recursive feature ranking on microarray data. Neural Networks, 2003. 16(5–6): pp.641-648.

DOI: 10.1016/s0893-6080(03)00103-5

[17] Blum, A.L. and P. Langley, Selection of relevant features and examples in machine learning. Artificial Intelligence, 1997. 97(1–2): pp.245-271.

DOI: 10.1016/s0004-3702(97)00063-5

[18] Zhe-Yuan, C., et al., Feature Selection Algorithm Based on Kernel Distance Measure. PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2010. 23(2): pp.235-240.

[19] Yang, M. and Z. Fang, Feature selection algorithm based on dynamic programming and comentropy. COMPUTER ENGINEERING AND DESIGN, 2010. 31(17): pp.3879-3881, 3894.

[20] Ding, C. and H. Peng. Minimum redundancy feature selection from microarray gene expression data. 2003. Piscataway, NJ, USA: IEEE Comput. Soc.

DOI: 10.1109/csb.2003.1227396

[21] Hua, J., W.D. Tembe and E.R. Dougherty, Performance of feature-selection methods in the classification of high-dimension data. Pattern Recognition, 2009. 42(3): pp.409-424.

DOI: 10.1016/j.patcog.2008.08.001

[22] Zhang, D., S. Chen and Z. Zhou, Constraint Score: A new filter method for feature selection with pairwise constraints. Pattern Recognition, 2008. 41(5): pp.1440-1451.

DOI: 10.1016/j.patcog.2007.10.009

[23] Michalak, K. and H. Kwasnicka, Correlation-based feature selection strategy in classification problems. 2006: pp.503-11.

[24] Ping, D. and L.I. Ning, A fast SVM-based feature selection method. JOURNAL OF SHANDONG UNIVERSITY(ENGINEERING SCIENCE), 2010. 40(5): pp.60-65.

[25] Ji-xiang, Y.E. and G. Xi-ling, A novel fast Wrapper for feature subset selection. JOURNAL OF CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY(NATURAL SCIENCE), 2010. 07(4): pp.69-73.

[26] Li G-Z, Yang J Y. Feature selection for ensemble learning and its application[M]. Machine Learning in Bioinformatics, 2008: 135-155.

DOI: 10.1002/9780470397428.ch6

[27] Yu-qing, W., et al., Image quality assessment based on local variance and structure similarity. JOURNAL OF OPTOELECTRONICS·LASER, 2008. 19(11): pp.1546-1553.

[28] Yun-wei, P.U., et al., Kernelized Xie-Beni index and its performance. CONTROL AND DECISION, 2007. 22(7): pp.829-832, 835.

[29] Zhang, X., et al., Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine. Expert Systems with Applications, 2012. 39(3): pp.2621-2628.

DOI: 10.1016/j.eswa.2011.08.117

[30] Jia, R. and G. Huang. Hydroelectric generating unit vibration fault diagnosis via BP neural network based on particle swarm optimization. 2009. Piscataway, NJ, USA: IEEE.

DOI: 10.1109/supergen.2009.5347991