Key Technologies for Health Management and Applications of Aero-Engine Based on Principal Component Analysis

Abstract:

Aero-Engine health management generally involves a series of activities over the period from the aerospace breaking down until it returning to normal, including signal processing, monitoring, health assessment, decision supporting, human-computer interaction, and so on. As one of the key technology of Aero-Engine health management, fault diagnosis plays a very important role on the safe operation of Aero-Engine. Currently, for effective challenging Aero-Engine health management, a fault diagnosis of Aero-Engine based on Principal Component Analysis (PCA) s proposed. Firstly, based on a variety of significant parameters of the collected information, principal component analysis model is established. Secondly, the fault diagnosis of engine operating conditions is realized by comparing the T² statistic and Squared Prediction Error (SPE) statistic as an engine running in good condition threshold limits. Finally, through the variable's cumulative contributions diagram with the behavior of SPE overrun, the fault variables are effectively worked out. Experimental results show that the proposed PCA method can efficiently come true Aero-Engine health management o and has some engineering applications values.

• Periodical: Applied Mechanics and Materials (Volume 538)
• Pages: 218-221
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.538.218
• Online since: April 2014
• Authors: Jian Guo Cui*, Xu Zhao, Jun Li, Bo Cui, Li Ying Jiang, Jian Qiang Shi
• Keywords: Aero-Engine, Fault Diagnosis, Gas Path System, Health Management, Principal Component Analysis (PCA)

References

[1] Z. F. Gong, Q. Y. Gong. Aero-Engine Sensor fault diagnosis based on Principal Component Analysis [J]. The computer measurement and control, 2012, 20(8): 2017-(2019).

[2] R. Li, Q. Y. Gong. Aero-Engine sensor fault diagnosis comprehensive simulation platform design and validation. The computer measurement and control, 2010, 18(3): 527-529.

[3] F. Yang, J. H. Hu, W. Chen. Principal Component Analysis Method in Aero-engine fault detection and diagnosis [J]. Mechanical Science and Technology, 2008, 27(3): 330-333.

[4] J. L. Mao, M. Li, L. Jian. The Research of Gas Path Trouble Diagnosing for a Given Turbo-Shaft Engine in the Stable Condition [J], Journal of Aerospace Power, 2002, 17(1): 69-74.

[5] A. Kasperski, M. Kulej. The 0-1 Knapsack Problem with Fuzzy Data. FuzzyOptimization and Decision Making. 2007, 6(2): 163~172.

DOI: 10.1007/s10700-007-9000-3