NDLI: Damage level recognition for planetary gearbox in rotorcraft based on GRA and ANN

Content Provider	IEEE Xplore Digital Library
Author	Cheng Zhe Hu Niaoqing Hou Weiyu Dong Hongqiang Zhang Ming
Copyright Year	2014
Description	Author affiliation: Sci. & Technol. on Integrated Logistics Support Lab., Nat. Univ. of Defense Technol., Changsha, China (Cheng Zhe; Hu Niaoqing; Hou Weiyu) \|\| Found. Educ. Coll. of Command Officer, Nat. Univ. of Defense Technol., Changsha, China (Dong Hongqiang; Zhang Ming)
Abstract	Planetary gearbox is a common mechanical component and is widely used to transmit power and change speed and/or direction in rotary aircrafts. The part failure of planetary gearbox is one of the main causes for the helicopter accidents. The need to identify the level of developing damage in part is central to reduce mechanically induced failures. An approach based on grey relational analysis(GRA) and artificial neural net (ANN) is presented to recognize the damage level quantitatively for planetary gearbox of rotorcraft. A particular emphasis is put on the feature selection based on GRA and the damage level recognition based on BP ANN. After that, the experiments with different-level-damage seeded are designed to validate the method above, and then the proposed method is used to identify the damage level based on test data. With the results of several experiments for damage level recognition, the feasibility and the effect of this approach are verified.
Starting Page	121
Ending Page	124
File Size	238100
Page Count	4
File Format	PDF
e-ISBN	9781479979585
DOI	10.1109/PHM.2014.6988146
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Vibrations Grey relational analysis Gears Artificial neural networks Damage seeded test component Artificial neural net Vectors Damage level recognition Feeds Planetary gearbox Biological neural networks
Content Type	Text
Resource Type	Article

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