Research on damage identification of hull girder based on Probabilistic Neural Network (PNN)

This study presents a damage identification method that combines wavelet packet transforms (WPTs) with neural network ensembles (NNEs). The WPT is used to extract damage features, which are taken as the input vectors in the NNEs used for damage identification. An experiment was performed on a helicopter rotor blades structure to verify the proposed method. First, the vibration responses collected by different sensors are decomposed using the WPT. Second, the relative band energy of each decomposed frequency band is calculated and fused as the damage feature vectors. Third, two types of the NNEs are designed. One is based on the backward propagation neural networks (BPNNs) for detecting the damage locations and severities and the other one is based on the probabilistic neural network (PNN) to detect the damage types. Finally, the trained NNEs are employed in damage identification. From the identification outcomes, it is concluded that damage information can be extracted effectively by the WPT and the identification accuracy of the NNEs is better than that of individual neural networks (INNs).

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Low energy impact damage identification method of CFRP structure based on wavelet transform and probabilistic neural network

Optik ◽

10.1016/j.ijleo.2021.166490 ◽

2021 ◽

Vol 232 ◽

pp. 166490

Author(s):

Shizeng Lu ◽

Huijun Dong ◽

Rongfeng Zhang ◽

Hongliang Yu

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Damage Identification ◽

Impact Damage ◽

Probabilistic Neural Network ◽

Low Energy ◽

Identification Method ◽

Energy Impact

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Substructural Damage Identification of Stone Arch Bridge Based on Probabilistic Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.178-181.2433 ◽

2012 ◽

Vol 178-181 ◽

pp. 2433-2438

Author(s):

Feng Qi Guo ◽

Zhi Wu Yu

Keyword(s):

Neural Network ◽

Numerical Model ◽

Damage Identification ◽

Probabilistic Neural Network ◽

Vertical Wall ◽

Static Displacement ◽

Arch Bridge ◽

Network Training ◽

Noise Data ◽

Main Arch

Stone arch bridge was divided into three substructures. They were main arch, vertical wall and vice arch or carriageway board. Probabilistic neural network was applied to substructural damage identification. Static displacement and low-order frequencies were taken as input parameters of the network training. A numerical model was developed to simulate the process of substructural damage identification of stone arch bridge. The effects of noise data to training and recognition were researched. The results show that it is feasible and effective to use probabilistic neural network in substructural damage identification of stone arch bridge.

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Intelligent Data-Fusion Model Using Correlation Fractal Dimension for Structural Damage Identification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.1300 ◽

2010 ◽

Vol 143-144 ◽

pp. 1300-1304

Author(s):

Shao Fei Jiang ◽

Chun Fu ◽

Zhao Qi Wu

Keyword(s):

Neural Network ◽

Fractal Dimension ◽

Data Fusion ◽

Structural Damage ◽

Damage Identification ◽

Probabilistic Neural Network ◽

Fractal Theory ◽

Noise Levels ◽

Fusion Model ◽

Intelligent Model

To extract effectively features and improve damage identification precision, this study proposed an intelligent data-fusion model by integrating fractal theory, probabilistic neural network (PNN) and data fusion. A two-span concrete-filled steel tubular arch bridge in service was used to validate the intelligent model by identifying both single- and multi-damage patterns. The results show that the intelligent model proposed can not only reliably identify damage with different noise levels, but also have an excellent anti-noise capability and robustness.

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Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

Scientific Research Journal ◽

10.24191/srj.v2i2.9330 ◽

2005 ◽

Vol 2 (2) ◽

pp. 25

Author(s):

Noraliza Hamzah ◽

Wan Nor Ainin Wan Abdullah ◽

Pauziah Mohd Arsad

Keyword(s):

Neural Network ◽

Power Quality ◽

Probabilistic Neural Network ◽

Microsoft Excel ◽

Waveform Data ◽

The Neural Network ◽

Harmonic Voltage ◽

Power Quality Disturbances ◽

Electronic Load ◽

Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code. Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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