Structural Health Monitoring of Lattice Structure Using Artificial Neural Network

2021 ◽  
pp. 1081-1089
Author(s):  
Christeena Jose ◽  
Prince Thankachan ◽  
T. M. Madhavan Pillai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Lattice Structure ◽  
Structural Health ◽  
Artificial Neural

SHM System Based on ANN for Aeronautical Applications

2011 ◽  
Vol 495 ◽  
pp. 129-133
Author(s):  
Christos Katsikeros ◽  
Claudio Sbarufatti ◽  
George Lampeas ◽  
Ioannis Diamantakos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Stiffened Panels ◽  
Network Layout ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Ann Application

In the present work a Structural Health Monitoring (SHM) system based on the use of Artificial Neural Network (ANN) method is presented that is suitable for aeronautical applications. The proposed methodology can be applied for the case of stiffened panels that are typical in aeronautical structures. The effect of sensor network layout, as well as noise applied during the training and prediction phase of the ANN application, is examined.

An Artificial Neural Receptor System for Structural Health Monitoring

2005 ◽  
Vol 4 (3) ◽  
pp. 229-245 ◽  
Author(s):  
W. N. Martin ◽  
A. Ghoshal ◽  
M. J. Sundaresan ◽  
G. L. Lebby ◽  
P. R. Pratap ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Receptor System ◽  
Structural Health ◽  
Artificial Neural

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Enhancing Vibration-Based Structural Health Monitoring via Edge Computing: A Tiny Machine Learning Perspective

2021 ◽  
Author(s):  
Federica Zonzini ◽  
Francesca Romano ◽  
Antonio Carbone ◽  
Matteo Zauli ◽  
Luca De Marchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Network Architecture ◽  
Binary Classification ◽  
Low Cost ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Structural Health

Abstract Despite the outstanding improvements achieved by artificial intelligence in the Structural Health Monitoring (SHM) field, some challenges need to be coped with. Among them, the necessity to reduce the complexity of the models and the data-to-user latency time which are still affecting state-of-the-art solutions. This is due to the continuous forwarding of a huge amount of data to centralized servers, where the inference process is usually executed in a bulky manner. Conversely, the emerging field of Tiny Machine Learning (TinyML), promoted by the recent advancements by the electronic and information engineering community, made sensor-near data inference a tangible, low-cost and computationally efficient alternative. In line with this observation, this work explored the embodiment of the One Class Classifier Neural Network, i.e., a neural network architecture solving binary classification problems for vibration-based SHM scenarios, into a resource-constrained device. To this end, OCCNN has been ported on the Arduino Nano 33 BLE Sense platform and validated with experimental data from the Z24 bridge use case, reaching an average accuracy and precision of 95% and 94%, respectively.

scholarly journals Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring

2020 ◽  
Vol 10 (3) ◽  
pp. 839 ◽  
Author(s):  
Tzu-Kang Lin ◽  
Yu-Ching Chen
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Confusion Matrix ◽  
Sample Entropy ◽  
Ambient Vibrations ◽  
Analysis Software ◽  
Element Analysis ◽  
Structural Health ◽  
Training Samples

This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.

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