The aero-engine gas-path electrostatic monitoring system is capable of providing early warning of impending gas-path component faults. In the presented work, a method is proposed to acquire signal sample under a specific operating condition for on-line fault detection. The symbolic time-series analysis (STSA) method is adopted for the analysis of signal sample. Advantages of the proposed method include its efficiency in numerical computations and being less sensitive to measurement noise, which is suitable for in situ engine health monitoring application. A case study is carried out on a data set acquired during a turbojet engine reliability test program. It is found that the proposed symbolic analysis techniques can be used to characterize the statistical patterns presented in the gas path electrostatic monitoring data (GPEMD) for different health conditions. The proposed anomaly measure, i.e., the relative entropy derived from the statistical patterns, is confirmed to be able to indicate the gas path components faults. Finally, the further research task and direction are discussed.
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October 2017
Research-Article
Symbolic Time-Series Analysis of Gas Turbine Gas Path Electrostatic Monitoring Data
Jianzhong Sun,
Jianzhong Sun
College of Civil Aviation,
Nanjing University of Aeronautics
and Astronautics,
Nanjing 211106, China
e-mail: sunjianzhong@nuaa.edu.cn
Nanjing University of Aeronautics
and Astronautics,
Nanjing 211106, China
e-mail: sunjianzhong@nuaa.edu.cn
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Pengpeng Liu,
Pengpeng Liu
System Engineering Research Institute,
China State Shipbuilding Corporation,
Beijing 100036, China
e-mail: liutianyu221@163.com
China State Shipbuilding Corporation,
Beijing 100036, China
e-mail: liutianyu221@163.com
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Yibing Yin,
Yibing Yin
College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: yinyibing1992@163.com
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: yinyibing1992@163.com
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Hongfu Zuo,
Hongfu Zuo
College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: rms@nuaa.edu.cn
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: rms@nuaa.edu.cn
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Chaoyi Li
Chaoyi Li
College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: lichaoyi_nuaa@163.com
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: lichaoyi_nuaa@163.com
Search for other works by this author on:
Jianzhong Sun
College of Civil Aviation,
Nanjing University of Aeronautics
and Astronautics,
Nanjing 211106, China
e-mail: sunjianzhong@nuaa.edu.cn
Nanjing University of Aeronautics
and Astronautics,
Nanjing 211106, China
e-mail: sunjianzhong@nuaa.edu.cn
Pengpeng Liu
System Engineering Research Institute,
China State Shipbuilding Corporation,
Beijing 100036, China
e-mail: liutianyu221@163.com
China State Shipbuilding Corporation,
Beijing 100036, China
e-mail: liutianyu221@163.com
Yibing Yin
College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: yinyibing1992@163.com
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: yinyibing1992@163.com
Hongfu Zuo
College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: rms@nuaa.edu.cn
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: rms@nuaa.edu.cn
Chaoyi Li
College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: lichaoyi_nuaa@163.com
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: lichaoyi_nuaa@163.com
Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received September 14, 2016; final manuscript received March 22, 2017; published online May 9, 2017. Assoc. Editor: Allan Volponi.
J. Eng. Gas Turbines Power. Oct 2017, 139(10): 102603 (7 pages)
Published Online: May 9, 2017
Article history
Received:
September 14, 2016
Revised:
March 22, 2017
Citation
Sun, J., Liu, P., Yin, Y., Zuo, H., and Li, C. (May 9, 2017). "Symbolic Time-Series Analysis of Gas Turbine Gas Path Electrostatic Monitoring Data." ASME. J. Eng. Gas Turbines Power. October 2017; 139(10): 102603. https://doi.org/10.1115/1.4036492
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