Research Papers: Gas Turbines: Turbomachinery

Symbolic Time-Series Analysis of Gas Turbine Gas Path Electrostatic Monitoring Data

[+] Author and Article Information
Jianzhong Sun

College of Civil Aviation,
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

Yibing Yin

College of Civil Aviation,
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

Chaoyi Li

College of Civil Aviation,
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 139(10), 102603 (May 09, 2017) (7 pages) Paper No: GTP-16-1450; doi: 10.1115/1.4036492 History: Received September 14, 2016; Revised March 22, 2017

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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Fig. 1

Principle of the engine gas path electrostatic monitoring

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Fig. 2

Charged particle(s) passing along the sensor probe

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Fig. 3

The equivalent measurement circuit of the electrostatic monitoring system

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Fig. 4

The gas path electrostatic monitoring system

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Fig. 5

Experiment setup on turbojet engine

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Fig. 6

GPEMD of one test cycle

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Fig. 9

Symbol sequence histogram from the GPEMD in the nominal state

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Fig. 10

Anomaly measure calculated from GPEMD using the symbolic analysis method

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Fig. 11

The relative entropy against the oil consumption

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Fig. 7

A sample signal under a specific operating condition (nominal state)

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Fig. 8

Sample signals under a specific operating condition (anomalous state)

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Fig. 12

The symbol-sequence histogram from the GPEMD at 10/13

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Fig. 13

The symbol-sequence histograms from the GPEMD at 11/24



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