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Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation

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  • Yang, Xilian
  • Zhao, Qunfei
  • Wang, Yuzhang
  • Cheng, Kanru

Abstract

Improving efficiency through intelligence is the current development trend of industrial gas turbines. Among the fault statistics of gas turbines, the number of sensor fault is the highest during use. The fault signal diagnosis and reconstruction are of great significance to the efficient and safe operation of gas turbines. In order to eliminate the sensor fault signal and transmit the normal signal to the control system, a multivariate fault signal reconstruction method based on the prior knowledge of the time-series representation was proposed in this work. The proposed multivariate signal reconstruction method can reconstruct almost all fault cases with high accuracy by training only one model. Firstly, the prior knowledge is applied to improve the conventional time series data representation. Secondly, three steps are employed to utilize spatial or temporal information and obtain three intermediate data. The masks combine the third intermediate data and the original time series to obtain the final reconstruction results. Then, reconstruction data sets are built based on exhaust gas temperatures (EGTs) from real-world power plant to verify the effectiveness of the proposed method. Different evaluation metrics and visualization reveal the high accuracy results. Compared results with different reconstruction algorithms reflect both the robustness and high speed of this reconstruction method. Finally, three typical fault signal reconstruction cases reveal the generalizability of this model.

Suggested Citation

  • Yang, Xilian & Zhao, Qunfei & Wang, Yuzhang & Cheng, Kanru, 2023. "Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s036054422201893x
    DOI: 10.1016/j.energy.2022.124996
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    1. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    2. Yang, Chunzhen & Liu, Jingquan & Zeng, Yuyun & Xie, Guangyao, 2019. "Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model," Renewable Energy, Elsevier, vol. 133(C), pages 433-441.
    3. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    4. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    5. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    6. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    7. Shi, Huiting & Chai, Jian & Lu, Quanying & Zheng, Jiali & Wang, Shouyang, 2022. "The impact of China's low-carbon transition on economy, society and energy in 2030 based on CO2 emissions drivers," Energy, Elsevier, vol. 239(PD).
    8. Gallo, Marco & Costabile, Carmine & Sorrentino, Marco & Polverino, Pierpaolo & Pianese, Cesare, 2020. "Development and application of a comprehensive model-based methodology for fault mitigation of fuel cell powered systems," Applied Energy, Elsevier, vol. 279(C).
    9. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    10. Zhu, Jun-Wei & Zhou, Qiao-Qian & Wu, Li-Bing & Xu, Jian-Ming & Wang, Xin, 2021. "Topology reconstruction based fault identification for uncertain multi-agent systems with application to multi-axis motion control system," Applied Mathematics and Computation, Elsevier, vol. 399(C).
    11. Yang, Jie & Ma, Tieding & Ma, Kai & Yang, Bo & Guerrero, Josep M. & Liu, Zhixin, 2021. "Trading mechanism and pricing strategy of integrated energy systems based on credit rating and Bayesian game," Energy, Elsevier, vol. 232(C).
    12. Hui, Jiuwu & Yuan, Jingqi, 2022. "Neural network-based adaptive fault-tolerant control for load following of a MHTGR with prescribed performance and CRDM faults," Energy, Elsevier, vol. 257(C).
    13. Jinfu Liu & Zhenhua Long & Mingliang Bai & Linhai Zhu & Daren Yu, 2021. "A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems," Energies, MDPI, vol. 14(2), pages 1-31, January.
    14. Wang, Yuzhang & Zhang, Qing & Li, Yixing & He, Ming & Weng, Shilie, 2022. "Research on the effectiveness of the key components in the HAT cycle," Applied Energy, Elsevier, vol. 306(PB).
    15. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2022. "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, Elsevier, vol. 305(C).
    16. Sun, Rongzhuo & Shi, Licheng & Yang, Xilian & Wang, Yuzhang & Zhao, Qunfei, 2020. "A coupling diagnosis method of sensors faults in gas turbine control system," Energy, Elsevier, vol. 205(C).
    Full references (including those not matched with items on IDEAS)

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