WANG Anna, SHA Mo, LIU Limei, et al., “A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 258-262, 2015, doi: 10.1049/cje.2015.04.006
Citation: WANG Anna, SHA Mo, LIU Limei, et al., “A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 258-262, 2015, doi: 10.1049/cje.2015.04.006

A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM

doi: 10.1049/cje.2015.04.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61050006).
More Information
  • Corresponding author: SHA Mo was born in 1979. He received the M.S. degree from Northeastern University of China in 2006. His current research interests include machine learning and fault diagnosis. (Email:desert1979@163.com)
  • Publish Date: 2015-04-10
  • Support vector machine (SVM) is an effective tool in deal with small sample, nonlinear and high dimension classification problems. In this paper, an improved pre-treatment binary-tree SVM is proposed to solve fault diagnosis. Furthermore an ensemble method is presented to establish ensemble SVM. Here the improved SVM is used as weak learning machine. The new ensemble SVM can improve the performance of single binary-tree SVM. At the end, the new algorithm is applied to fault diagnosis of blast furnace faults and the Tennessee Eastman process (TEP). The experiments results show that the improved binary-tree SVM algorithm has an excellent performance on diagnosis speed and accuracy.
  • loading
  • B.E. Boser, I.M. Guyon and V.N. Vapnik, “A training algorithm for optimal margin classifiers”, The 5th Annual ACM Workshop on COLT, Pittsburgh, PA, USA, pp.144-152, 1992.
    C. Cortes and V.N. Vapnik, “Support vector networks”, Machine Learning, Vol.20, No.3, pp.273-297, 1995.
    N.Y. Deng and Y.J. Tian, Support Vector Machines: A New Method in Data Mining, Science Press, Beijing, China, pp.32-36, 2004. (in Chinese)
    T.G. Dirtterich and G. Bakiri, “Solving multi-class learning problem via error-correcting output codes”, Journal of Artificial Intelligent Research, Vol.2, No.3, pp.263-286, 1995.
    D. Tsujinishi and S. Abe, “Fuzzy least squares support vector machines for multi-class problem”, Neural Networks, Vol.16, No.3, pp.785-792, 2003.
    T. Inoue and S. Abe, “Fuzzy support vector machines for pattern classification”, In proceedings of International Joint conference on neural networks, Washington, DC, USA, pp.1449-1454, 2001.
    Cheong Sungmoon, Oh Sang-Hong and Lee Soo-Young, “Support vector machines with binary tree architecture for multiclass classification”, Neural Information Processing-Letters and Reviews, Vol.2, No.3, pp.47-51, 2004.
    N. Duffy and D. Helmbold, “Boosting methods for regression”, Machine Learning, Vol.47, No.2, pp.153-200, 2004.
    C. Crouxa, K. Joossensa and A. Lemmensb, “Trimmed bagging”, Computational Statistics and Data Analysis, Vol.52, No.2, pp.362-368, 2006.
    L. Breiman, “Bagging predictors”, Machine Learning, Vol.24, No.2, pp.123-140, 1996.
    J.J. Downs and E.F. Vogel, “A plant-wide industrial-process control problem”, Computers and Chemical Engineering, Vol.17, No.3, pp.245-255, 1993.
    L.H. Chiang, M.E. Kotanchek and A.K. Kordon, “Fault diagnosis based on Fisher discriminant analysis and support vector machines”, Computers and Chemical Engineering, Vol.24, No.3, pp.1389-1401, 2004.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (542) PDF downloads(1068) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return