Volume 17, Issue 7 (9-2017)                   Modares Mechanical Engineering 2017, 17(7): 363-372 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Khazaee M, Salehzadeh Nobari A, Khazaee M. Damage detection in Glass Fiber Reinforced Plastic (GFRP) using neural network based on denoising with different mother wavelets. Modares Mechanical Engineering 2017; 17 (7) :363-372
URL: http://mme.modares.ac.ir/article-15-1508-en.html
Abstract:   (3975 Views)
In this paper, a vibration-based damage detection approach for multi-layered woven glass laminate using time signal processing and Neural Network (NN) is presented. In order to reduce noise in the experimental extracted signals, wavelet-based denoising has been applied. After data mining and feature extraction from processed signals, NN as a classifier is employed to detect the damaged GFRP. Different NN structures were tested in order to enhance the damage detection performance to recognize the most remarkable performance. Also, the performance of the presented method was evaluated when different mother of wavelets at different decomposition levels denoise signals so that the best signal processing method is selected. The results demonstrate the effect of NN structure on the damage detection technique, which in this research the best NN performance was obtained with 75 hidden layers and allocating 80%, 10% and 10% of data to training, evaluation and testing, respectively. Furthermore, denoising using db3 and bior3.7 mother wavelets at 2nd decomposition level leads to the highest accuracy as well as suitable calculation time compared to other mother wavelets. The proposed method based on real data at the data acquisition points detects damage in composite laminate with high accuracy at reasonable calculation time, hence it can be used for condition monitoring of composite laminate either offline or online, provided that adding online data acquisition equipment.
Full-Text [PDF 1400 kb]   (5563 Downloads)    
Article Type: Research Article | Subject: Non Destvuctive Test
Received: 2017/03/6 | Accepted: 2017/06/4 | Published: 2017/08/4

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.