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Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers

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  • Bai, Mingliang
  • Yang, Xusheng
  • Liu, Jinfu
  • Liu, Jiao
  • Yu, Daren

Abstract

Gas turbine combustion chambers work in highly adverse environment and thus malfunction more easily compared to other components. Fault detection of gas turbine combustion chambers can significantly increase the safety and reliability. Current researches on data-driven fault detection require abundant historical data. However, for a new gas turbine that has just operated for a short time, the available historical data are quite few, and the available fault data are even fewer. To address this problem, this paper first proposes the concept of gas turbine group fault diagnosis. Deep transfer learning is introduced into the fault detection of gas turbine combustion chambers for the first time. Convolutional neural network is pretrained using the data from one data-rich gas turbine and the pretrained convolutional neural network is finetuned for fault detection of another data-poor gas turbine. Through deep transfer learning, fault knowledge is shared between one data-rich gas turbine and another data-poor gas turbine. Experiments in two actual gas turbines show the proposed method can obtain good detection performance. Further comparison experiments show that the proposed method can significantly improve the detection performance compared with using the data from data-poor gas turbine directly and mixing the data from data-rich gas turbine and data-poor gas turbine. Detailed visualization analysis is made to explain what common fault knowledge is shared. This can also explain why deep transfer learning and gas turbine group fault diagnosis are effective to some extent.

Suggested Citation

  • Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:appene:v:302:y:2021:i:c:s0306261921008916
    DOI: 10.1016/j.apenergy.2021.117509
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    7. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
    8. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).

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