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Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

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  • Salahshoor, Karim
  • Kordestani, Mojtaba
  • Khoshro, Majid S.

Abstract

The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults.

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  • Salahshoor, Karim & Kordestani, Mojtaba & Khoshro, Majid S., 2010. "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers," Energy, Elsevier, vol. 35(12), pages 5472-5482.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:12:p:5472-5482
    DOI: 10.1016/j.energy.2010.06.001
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    1. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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    1. Salman Khalid & Jinwoo Song & Izaz Raouf & Heung Soo Kim, 2023. "Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    2. Huang, Chung-Neng & Chen, Yui-Sung, 2017. "Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles," Energy, Elsevier, vol. 118(C), pages 840-852.
    3. Huang, Bo & Peng, Yun-Hong & Hu, Li-Sheng & Liang, Xiao-Chi, 2024. "Incipient fault detection approach based on piecewise linear shape-based global embedding for steam turbine plants," Applied Energy, Elsevier, vol. 370(C).
    4. Strušnik, Dušan & Avsec, Jurij, 2015. "Artificial neural networking and fuzzy logic exergy controlling model of combined heat and power system in thermal power plant," Energy, Elsevier, vol. 80(C), pages 318-330.
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    6. Luo, Xianglong & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2011. "Modeling and optimization of a utility system containing multiple extractions steam turbines," Energy, Elsevier, vol. 36(5), pages 3501-3512.
    7. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
    8. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    9. Morshedizadeh, Majid & Kordestani, Mojtaba & Carriveau, Rupp & Ting, David S.-K. & Saif, Mehrdad, 2017. "Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production," Energy, Elsevier, vol. 138(C), pages 394-404.
    10. Yang, Jaemin & Kim, Jonghyun, 2020. "Accident diagnosis algorithm with untrained accident identification during power-increasing operation," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    11. Yaïci, Wahiba & Entchev, Evgueniy, 2016. "Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system," Renewable Energy, Elsevier, vol. 86(C), pages 302-315.
    12. Kumar, Manoj & Behera, Suraj K. & Kumar, Amitesh & Sahoo, Ranjit K., 2019. "Numerical and experimental investigation to visualize the fluid flow and thermal characteristics of a cryogenic turboexpander," Energy, Elsevier, vol. 189(C).
    13. Jungwon Yu & Jaeyel Jang & Jaeyeong Yoo & June Ho Park & Sungshin Kim, 2018. "A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant," Energies, MDPI, vol. 11(5), pages 1-19, May.
    14. Hu, Pengfei & Cao, Lihua & Su, Jingkai & Li, Qi & Li, Yong, 2020. "Distribution characteristics of salt-out particles in steam turbine stage," Energy, Elsevier, vol. 192(C).
    15. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    16. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    17. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    18. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    19. 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).

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