Damage Identification of Composite Materials Based on PNN

Dong, Xiao Ma

doi:10.4028/www.scientific.net/AMM.29-32.642

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Study of the Modeling of the Gear Dynamics Considering Mesh Stiffness and Sliding Friction
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The Compound Tribological Properties of Nano-Ceramic Lubricating Additives and Ni-W-P Alloy Coating
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Study of Compatibility of the Nano Lubricating Additives with the Electroless Deposited Ni-W-P Coating
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The Non-Linear Contact Finite Element Analysis of Seal Structure on Large Scale Cutting Tube Machine
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Damage Identification of Composite Materials Based on PNN
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Moldflow Software in a Complex Plastic Shell Injection Mold Design
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Numerical Simulation and Research of Pool Fire Suppressed by Water Mist in the Engineroom of a Ship
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Calculating Method for the Solid Phase Effect Coefficient of the Solid-Fluid Two Phase Flow Centrifugal Pump
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Fatigue Properties of TCS Stainless Steel Weld Joints in Axial Loading
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HomeApplied Mechanics and MaterialsApplied Mechanics And Mechanical EngineeringDamage Identification of Composite Materials Based...

Damage Identification of Composite Materials Based on PNN

Abstract:

In recent years, there were been increasing researches focusing on the application of artificial neural networks in structural damage identification. Most of them perform well with numerical examples under error-free conditions, but become worse when the experimental data are polluted with measurement noise. In this paper, a dynamic approach based on PNN for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to PNN for training the network and identifying the damages. A performance comparison between the PNN and BPNN for structural damage identification was carried out. The results show that the proposed method can more exactly identify the faults than the BP neural network.

Info:

Periodical:

Applied Mechanics and Materials (Volumes 29-32)

Main Theme:

Applied Mechanics And Mechanical Engineering

Edited by:

Honghua Tan

Pages:

642-645

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.29-32.642

Citation:

X. M. Dong "Damage Identification of Composite Materials Based on PNN", Applied Mechanics and Materials, Vols. 29-32, pp. 642-645, 2010

Online since:

August 2010

Authors:

Xiao Ma Dong

Keywords:

BP Neural Network (BPNN), Composite Material, PNN, Wavelet

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$38.00

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References

[1] Islam A S, Cralg K C. Damage detection in composite structures using piezoelectric materials[J]. Smart Materials and Structures, 1994, (3): 318~328.

[2] Xiao-ma Dong, Chun-feng Pan, Guang-hui Li. Steel Truss Girder Bridge Damage Alarm Research Base on Hybrid Algorithm RBFNN[ C ]. ICMS2009, 2009, (5): 393~396.

[3] Luo H, Hanagud S. Dynamic learning rate neural network training and composite structural damage detection[ J ]. AIAA Journal, 1997, (35): 1552~1527.

DOI: https://doi.org/10.2514/2.7480

[4] Wen Jin; Zhao Jia; Luo Si Wei; Han Zhen. The improvements of BP neural network learning algorithm[ J ]. Signal Processing Proceedings, 2000, (3): 1647~1649.

DOI: https://doi.org/10.1109/icosp.2000.893417

[5] Dong Xiaoma, Sun Qingzhen, Wei Baoli, Hou Xiaoying. Research on damage detection of frame structues based on wavelet analysis and norm space. ICIII2009, (2): 39~41.

DOI: https://doi.org/10.1109/iciii.2009.167

[6] Specht D F. Enhancements to probabilistic neural networks. In: Proceedings of the IEEEInternational Joint Conference on Neural Networks, Baltimore, MD, June 7-11, (1992).

DOI: https://doi.org/10.1109/ijcnn.1992.287095

[7] Mottershead, J. E, M.I. Friswell. Model updating in structure dynamics: A survey[J]. Journal of Sound and Vibration, 1993, 167(2): 347~37.

[8] Dong Xiaoma, Zhang Weigong. Modified BP ANN Based Dynamic Measurement of Wheel Alignment Parameters[J]. Automobile Technology, 2003, 12: 31~33.

Paper TitlePages

Structural Damage Localization Based on AR Model and BP Neural Network

Authors: Yan Song Diao, Fei Yu, Dong Mei Meng

Abstract: When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.

1211

The Dynamic Testing Method for Damage Diagnosis of Multi-Story Masonry Buildings

Authors: Feng Ge Li, Hong Yu

Abstract: A new method to diagnose the damage of masonry structure using artificial network is brought forward. The frequencies and the ratio of change for any two orders various frequencies is chosen as a damage indicator and is used to train the BP neural network, which can diagnose the damage of masonry structure. The result of the numerical computation shows that the BP neural network can diagnose the damage of masonry structure.

1148

Application of BP Neural Network in Structural Damage Diagnosis of Bridge behind Abutment

Authors: Yu Hui Zhang

Abstract: BP neural network is introduced and applied to identify and diagnose both location and extent of bridge structural damage; static load tests and dynamic calculations are also made on bridge structural damage behind abutment. The key step of this method is to design a reasonably perfect BP network model. According to the current knowledge, three BP neural networks are designed with horizontal displacement rate and inherent frequency rate as damage identification indexes. The neural networks are used to identify the measurement of structure behind abutment and the calculation of damage location and extent, at the same time, they can also be used to compare and analyze the results. The test results show that: taking the two factors (static structural deformation rate and the change rate of natural frequency in dynamic response) as input vector, the BP neural network can accurately identify the damage location and extent, implying a promising perspective for future applications.

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