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Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Author

Listed:
  • Nicola Menga

    (Department of Engineering for Innovation, University of Salento, Via Monteroni, 73100 Lecce, Italy)

  • Akhila Mothakani

    (Department of Engineering for Innovation, University of Salento, Via Monteroni, 73100 Lecce, Italy)

  • Maria Grazia De Giorgi

    (Department of Engineering for Innovation, University of Salento, Via Monteroni, 73100 Lecce, Italy)

  • Radoslaw Przysowa

    (Instytut Techniczny Wojsk Lotniczych (ITWL), ul. Ksiecia Boleslawa 6, 01-494 Warsaw, Poland)

  • Antonio Ficarella

    (Department of Engineering for Innovation, University of Salento, Via Monteroni, 73100 Lecce, Italy)

Abstract

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

Suggested Citation

  • Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7304-:d:933487
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    References listed on IDEAS

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    1. Martí de Castro-Cros & Manel Velasco & Cecilio Angulo, 2021. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review," Energies, MDPI, vol. 14(24), pages 1-27, December.
    2. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    3. Wei Jiang & Yanhe Xu & Yahui Shan & Han Liu, 2018. "Degradation Tendency Measurement of Aircraft Engines Based on FEEMD Permutation Entropy and Regularized Extreme Learning Machine Using Multi-Sensor Data," Energies, MDPI, vol. 11(12), pages 1-18, November.
    4. Ugochukwu Ejike Akpudo & Jang-Wook Hur, 2022. "Investigating the Efficiencies of Fusion Algorithms for Accurate Equipment Monitoring and Prognostics," Energies, MDPI, vol. 15(6), pages 1-18, March.
    5. Feng Lu & Chunyu Jiang & Jinquan Huang & Yafan Wang & Chengxin You, 2016. "A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis," Energies, MDPI, vol. 9(10), pages 1-22, October.
    6. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    7. Roberto Capata & Matteo Saracchini, 2018. "Experimental Campaign Tests on Ultra Micro Gas Turbines, Fuel Supply Comparison and Optimization," Energies, MDPI, vol. 11(4), pages 1-17, March.
    8. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    9. Xiaohuan Sun & Soheil Jafari & Seyed Alireza Miran Fashandi & Theoklis Nikolaidis, 2021. "Compressor Degradation Management Strategies for Gas Turbine Aero-Engine Controller Design," Energies, MDPI, vol. 14(18), pages 1-21, September.
    10. Hongyi Chen & Qiuhong Li & Shuwei Pang & Wenxiang Zhou, 2022. "A State Space Modeling Method for Aero-Engine Based on AFOS-ELM," Energies, MDPI, vol. 15(11), pages 1-15, May.
    11. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
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    1. Aygun, Hakan & Dursun, Omer Osman & Toraman, Suat, 2023. "Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes," Energy, Elsevier, vol. 271(C).

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