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Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester

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Abstract

The running status of hydraulic tube tester is reflected by the boosting pressure curve in Hydrostatic testing process. The authors present the extreme learning machine (ELM), a novel good learning scheme much faster than traditional gradient-based learning algorithms, as a mechanism for clustering the pressure curves. However, it caused low accuracy for clustering pressure curves for hydraulic tube tester. In this paper, a multi-stage ELM is proposed to improve the accuracy of clustering. During the process of this new ELM, the input data were divided into several stages, then, every stage was analyzed independently. At last, this method has been used in hydraulic tube tester data. Compared with individual ELM, it has better function for considering the characteristics of input data.

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Acknowledgments

This work was supported by the National Science Foundation of China under Grant 60374003 and project 973 under Grant 2002CB312200.

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Authors and Affiliations

  1. Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang, 110004, China

    Xue-fa Hu, Zhen Zhao, Shu Wang, Fu-li Wang & Da-kuo He

  2. Equipment Management Department, SteelTubing Plant, BaoSteel Branch, Shanghai, 201900, China

    Shui-kang Wu

Authors
  1. Xue-fa Hu
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  2. Zhen Zhao
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  3. Shu Wang
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  4. Fu-li Wang
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  5. Da-kuo He
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  6. Shui-kang Wu
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Corresponding author

Correspondence to Zhen Zhao.

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Hu, Xf., Zhao, Z., Wang, S. et al. Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester. Neural Comput & Applic 17, 399–403 (2008). https://doi.org/10.1007/s00521-007-0139-1

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  • DOI: https://doi.org/10.1007/s00521-007-0139-1

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