DAMAGE DETECTION IN GLASS FIBER REINFORCED PLASTIC (GFRP) USING NEURAL NETWORK BASED ON DENOISING WITH DIFFERENT MOTHER WAVELETS

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Khazaee Majid; SALEHZADEH NOBARI ALI; KHAZAEE MEGHDAD

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Journal: Modares Mechanical Engineering Year:2017 | Volume:17 | Issue:7 Page(s): 363-372

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Title

DAMAGE DETECTION IN GLASS FIBER REINFORCED PLASTIC (GFRP) USING NEURAL NETWORK BASED ON DENOISING WITH DIFFERENT MOTHER WAVELETS

Author(s)

Khazaee Majid | SALEHZADEH NOBARI ALI | KHAZAEE MEGHDAD | Issue Writer Certificate

Keywords

GFRPQ1

DAMAGE DETECTIONQ2

NEURAL NETWORKQ2

DENOISINGQ1

WAVELET TRANSFORMQ2

Abstract

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. Wavelet DENOISING technique has been applied in order to eliminate noise from the experimental extracted signals. After data mining and feature extraction from processed signals, NN is employed as a classifier to detect the damaged GFRP.Different NN structures were tested to recognize the most remarkable performance in DAMAGE DETECTION.Also, the presented method was evaluated when different mothers of wavelets at different decomposition levels denoise signals so that the best signal processing method could be selected.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 online data acquisition equipment is implemented.

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APA: Copy

Khazaee, Majid, SALEHZADEH NOBARI, ALI, & KHAZAEE, MEGHDAD. (2017). DAMAGE DETECTION IN GLASS FIBER REINFORCED PLASTIC (GFRP) USING NEURAL NETWORK BASED ON DENOISING WITH DIFFERENT MOTHER WAVELETS. MODARES MECHANICAL ENGINEERING, 17(7 ), 363-372. SID. https://sid.ir/paper/179346/en

Vancouver: Copy

Khazaee Majid, SALEHZADEH NOBARI ALI, KHAZAEE MEGHDAD. DAMAGE DETECTION IN GLASS FIBER REINFORCED PLASTIC (GFRP) USING NEURAL NETWORK BASED ON DENOISING WITH DIFFERENT MOTHER WAVELETS. MODARES MECHANICAL ENGINEERING[Internet]. 2017;17(7 ):363-372. Available from: https://sid.ir/paper/179346/en

IEEE: Copy

Majid Khazaee, ALI SALEHZADEH NOBARI, and MEGHDAD KHAZAEE, “DAMAGE DETECTION IN GLASS FIBER REINFORCED PLASTIC (GFRP) USING NEURAL NETWORK BASED ON DENOISING WITH DIFFERENT MOTHER WAVELETS,” MODARES MECHANICAL ENGINEERING, vol. 17, no. 7 , pp. 363–372, 2017, [Online]. Available: https://sid.ir/paper/179346/en

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