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Fault diagnosis of aero-engine endoscopic image processing based on BP neural network
Author(s): Xiaomin Xie; Fan Zhang; Yong Zeng; Changkai Li
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Paper Abstract

Aiming at the shortcomings of endoscopic image processing and detection technology in aero-engine fault diagnosis and maintenance, this paper proposes an endoscopic image processing and diagnosis method based on BP neural network learning algorithm. In this method, the feature extraction technology of endoscopic image in aero-engine fault diagnosis is studied, and the effective feature parameters are extracted from the internal damage region of aero-engine. The BP neural network model is established to improve the endoscopic image processing effect and improve the level of fault diagnosis. Finally, the BP neural network endoscopic image processing and diagnosis mechanism is simulated and experimentally studied for a Boeing 787 engine, and the expected diagnosis effect is achieved.

Paper Details

Date Published: 9 August 2018
PDF: 6 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108065K (9 August 2018); doi: 10.1117/12.2503051
Show Author Affiliations
Xiaomin Xie, Anhui Xinhua Univ. (China)
Fan Zhang, Anhui Xinhua Univ. (China)
Yong Zeng, Anhui Xinhua Univ. (China)
Changkai Li, Anhui Xinhua Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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