NDLI: Neural network modeling of aircraft power plant and fault diagnosis method using time frequency analysis

Content Provider	IEEE Xplore Digital Library
Author	Liao Wei Wang Hua Han Pu
Copyright Year	2009
Description	Author affiliation: Hebei University of Engineering, Handan 056038, China (Liao Wei; Wang Hua) \|\| North China Electric Power University, Baoding 071003, China (Han Pu)
Abstract	With the development of manufacturing engineer, the aeroengine structure and operating condition have become more complex and the circumstance is generally under mal-condition with high temperature and pressure, so keeping its reliability and safety of airplane is essential. An effective method for aeroengine fault diagnosis using wavelet neural networks is proposed. The wavelet transform can accurately detect and localize the characteristics of transient signal in time-frequency domain. The advantage of wavelet transform is in achieving flexible frequency resolution logarithmic time frequency bands, thus making it able to extract both high-frequency and low-frequency components from the vibration signal. The characteristic information obtained are input nodes of neural network for fault pattern recognition. The mathematics model for aeroengine fault diagnosis is established and the improved optimization technique for neural network training algorithm is used to accomplish the network parameter identification. By means of enough experiment samples to train the neural network, the fault mode can be obtained from the network output result. Furthermore, the robustness of wavelet network for fault diagnosis is discussed. The results obtained from the application of the method on monitored data collected from a facility validate the utility of this approach.
Starting Page	353
Ending Page	356
File Size	87528
Page Count	4
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5195079
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms reliability and safety Operation condition fault diagnosis Time frequency analysis Reliability engineering network parameter Aircraft manufacture training algorithm Aircraft propulsion neural network Aerospace engineering Fault diagnosis Neural networks Manufacturing transient signal Power generation
Content Type	Text
Resource Type	Article

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