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Research Papers

Life Cycle Performance Estimation and In-Flight Health Monitoring for Gas Turbine Engine

[+] Author and Article Information
Feng Lu

Associate Professor
Jiangsu Province Key Laboratory of
Aerospace Power Systems,
Nanjing University of
Aeronautics and Astronautics,
29 Yudao Street,
Nanjing, Jiangsu 210016, China;
Aviation Motor Control System Institute,
Aviation Industry Corporation of China,
792 Liangxi Road,
Wuxi, Jiangsu 214063, China
e-mail: lufengnuaa@126.com

Wenhua Zheng

Jiangsu Province Key Laboratory of
Aerospace Power Systems,
Nanjing University of
Aeronautics and Astronautics,
Nanjing, Jiangsu 210016, China;
Aviation Motor Control System Institute,
Aviation Industry Corporation of China,
792 Liangxi Road,
Wuxi, Jiangsu 214063, China
e-mail: lfaann@nuaa.edu.cn

Jinquan Huang

Jiangsu Province Key Laboratory of
Aerospace Power Systems,
Nanjing University of
Aeronautics and Astronautics,
29 Yudao Street,
Nanjing, Jiangsu 210016, China
e-mail: jhuang@nuaa.edu.cn

Min Feng

Aviation Motor Control System Institute,
Aviation Industry Corporation of China,
792 Liangxi Road,
Wuxi, Jiangsu 214063, China
e-mail: nuaafengmin@126.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 10, 2014; final manuscript received April 27, 2016; published online June 8, 2016. Assoc. Editor: Gregory Shaver.

J. Dyn. Sys., Meas., Control 138(9), 091009 (Jun 08, 2016) (13 pages) Paper No: DS-14-1523; doi: 10.1115/1.4033556 History: Received December 10, 2014; Revised April 27, 2016

A long-term gas-path fault diagnosis and its rapid prototype system are presented for on-line monitoring of a gas turbine engine. Toward this end, a nonlinear hybrid model-based performance estimation and abnormal detection method are proposed in this paper. An adaptive extended Kalman particle filter (AEKPF) estimator is developed and used to real time estimate engine health parameters, which depict gas turbine performance degradation condition. The health parameter estimators are then pushed into a buffer memory and for periodical renewing baseline model (BM) performance, and the BM is utilized to detect engine anomaly over its life course. The threshold in abnormal detection schemes is adapted to the modeling errors during the engine lifetime. The rapid prototyping system is designed and built up based on the National Instrument (NI) CompactRIO (CRIO) for evaluating gas turbine engine performance estimation and anomaly detection. A number of experiments are carried out to demonstrate the advantages of the proposed abnormal detection scheme and effectiveness of the designed rapid prototype system to the problem of gas turbine life cycle anomaly detection.

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Figures

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Fig. 1

Computational flow diagram of gas turbine engine simulation

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Fig. 3

Engine health-monitoring rapid prototype system

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Fig. 2

Life cycle engine health detection scheme

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Fig. 5

Engine health-monitoring results in case 1: (a) engine outputs from the measurements and the baseline and (b) health-monitoring results

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Fig. 6

Engine health-monitoring results in case 2: (a) engine outputs from the measurements and the baseline and (b) health-monitoring results

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Fig. 4

Performance comparisons of two state estimators from 1500 cycles to 3000 cycles: (a) nL, (b) T22, (c) P3, and (d) P6

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Fig. 7

Engine health-monitoring results in case 3: (a) engine outputs from the measurements and the baseline and (b) health-monitoring results

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Fig. 8

Engine health-monitoring results in case 4: (a) engine outputs from the measurements and the baseline and (b) health-monitoring results

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