Abstract A novel full wavefield processing method by using fully convolutional neural networks is... more Abstract A novel full wavefield processing method by using fully convolutional neural networks is presented. The full wavefield of propagating Lamb waves in the fibre-reinforced composite plate was simulated by the parallel spectral element method. It resembles a full wavefield measurements acquired on a surface of the plate by the scanning laser Doppler vibrometer. The aim of the proposed technique is an identification of delamination location, size and shape. It is achieved by pixel-wise image segmentation by using the end-to-end approach. It is possible because of the large dataset of Lamb wave propagation patterns resulting from interaction with delaminations of random location, size and shape. It is demonstrated that the proposed method, tested on numerical data, is performing better than conventional adaptive wavenumber filtering method which was developed in previous work. Moreover, it enables better automation of delamination identification so that the damage map can be created without user intervention. The method was also tested on experimental data acquired on the surface of the specimen in which delamination was artificially created by a Teflon insert. The obtained results with the deep learning approach show its capability to predict the delamination in the numerically generated dataset with high accuracy compared to the conventional damage detection approach. Furthermore, the deep learning model shows the ability to generalize to a further experiential set.
Full wavefield processing by using FCN for delamination detection, 2020
A novel full wavefield processing method by using fully convolutional neural networks is presente... more A novel full wavefield processing method by using fully convolutional neural networks is presented. The full wavefield of propagating Lamb waves in the fibre-reinforced composite plate was simulated by the parallel spectral element method. It resembles a full wavefield measurements acquired on a surface of the plate by the scanning laser Doppler vibrometer. The aim of the proposed technique is an identification of delamination location, size and shape. It is achieved by pixel-wise image segmentation by using the end-to-end approach. It is possible because of the large dataset of Lamb wave propagation patterns resulting from interaction with delaminations of random location, size and shape. It is demonstrated that the proposed method, tested on numerical data, is performing better than conventional adaptive wavenumber filtering method which was developed in previous work. Moreover, it enables better automation of delamination identification so that the damage map can be created without user intervention. The method was also tested on experimental data acquired on the surface of the specimen in which delamination was artificially created by a Teflon insert. The obtained results with the deep learning approach show its capability to predict the delamination in the numerically generated dataset with high accuracy compared to the conventional damage detection approach. Furthermore, the deep learning model shows the ability to generalize to a further experiential set.
Reinforced concrete poles are very popular in transmission lines due to their economic efficiency... more Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).
Abstract A novel full wavefield processing method by using fully convolutional neural networks is... more Abstract A novel full wavefield processing method by using fully convolutional neural networks is presented. The full wavefield of propagating Lamb waves in the fibre-reinforced composite plate was simulated by the parallel spectral element method. It resembles a full wavefield measurements acquired on a surface of the plate by the scanning laser Doppler vibrometer. The aim of the proposed technique is an identification of delamination location, size and shape. It is achieved by pixel-wise image segmentation by using the end-to-end approach. It is possible because of the large dataset of Lamb wave propagation patterns resulting from interaction with delaminations of random location, size and shape. It is demonstrated that the proposed method, tested on numerical data, is performing better than conventional adaptive wavenumber filtering method which was developed in previous work. Moreover, it enables better automation of delamination identification so that the damage map can be created without user intervention. The method was also tested on experimental data acquired on the surface of the specimen in which delamination was artificially created by a Teflon insert. The obtained results with the deep learning approach show its capability to predict the delamination in the numerically generated dataset with high accuracy compared to the conventional damage detection approach. Furthermore, the deep learning model shows the ability to generalize to a further experiential set.
Full wavefield processing by using FCN for delamination detection, 2020
A novel full wavefield processing method by using fully convolutional neural networks is presente... more A novel full wavefield processing method by using fully convolutional neural networks is presented. The full wavefield of propagating Lamb waves in the fibre-reinforced composite plate was simulated by the parallel spectral element method. It resembles a full wavefield measurements acquired on a surface of the plate by the scanning laser Doppler vibrometer. The aim of the proposed technique is an identification of delamination location, size and shape. It is achieved by pixel-wise image segmentation by using the end-to-end approach. It is possible because of the large dataset of Lamb wave propagation patterns resulting from interaction with delaminations of random location, size and shape. It is demonstrated that the proposed method, tested on numerical data, is performing better than conventional adaptive wavenumber filtering method which was developed in previous work. Moreover, it enables better automation of delamination identification so that the damage map can be created without user intervention. The method was also tested on experimental data acquired on the surface of the specimen in which delamination was artificially created by a Teflon insert. The obtained results with the deep learning approach show its capability to predict the delamination in the numerically generated dataset with high accuracy compared to the conventional damage detection approach. Furthermore, the deep learning model shows the ability to generalize to a further experiential set.
Reinforced concrete poles are very popular in transmission lines due to their economic efficiency... more Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).
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Papers by Saeed Ullah