Fiber optic vibration sensing and neural networks methods for prediction of composite beam delamination

1997 ◽  
Author(s):  
Gilbert W. Sanders ◽  
Farhad Akhavan ◽  
Steve E. Watkins ◽  
K. Chandrashekhara
Keyword(s):  
Neural Networks ◽  
Composite Beam ◽  
Fiber Optic ◽  
Vibration Sensing

Modal analysis using fiber optic sensors and neural networks for prediction of composite beam delamination

2002 ◽  
Vol 11 (4) ◽  
pp. 489-495 ◽  
Author(s):  
Steve E Watkins ◽  
Gilbert W Sanders ◽  
Farhad Akhavan ◽  
K Chandrashekhara
Keyword(s):  
Neural Networks ◽  
Modal Analysis ◽  
Composite Beam ◽  
Fiber Optic ◽  
Fiber Optic Sensors

scholarly journals Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers

2020 ◽  
Vol 28 (26) ◽  
pp. 39311
Author(s):  
Sascha Liehr ◽  
Christopher Borchardt ◽  
Sven Münzenberger
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Fiber Optic ◽  
Long Distance ◽  
Vibration Sensing

Distributed Fiber-Optic Acoustic Sensor for Sparse-wideband Vibration Sensing with Time Delay Sampling

2021 ◽  
pp. 1-1
Author(s):  
Yuanpeng Deng ◽  
Qingwen Liu ◽  
Zuyuan He
Keyword(s):  
Time Delay ◽  
Fiber Optic ◽  
Acoustic Sensor ◽  
Vibration Sensing

Distributed single fiber optic vibration sensing with high frequency response and multi-points accurate location

2020 ◽  
Vol 129 ◽  
pp. 106060 ◽  
Author(s):  
Pengfei Ma ◽  
Kun Liu ◽  
Zhenshi Sun ◽  
Junfeng Jiang ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Frequency Response ◽  
High Frequency ◽  
Fiber Optic ◽  
Single Fiber ◽  
High Frequency Response ◽  
Vibration Sensing

Fiber Optic Multi-parameter Sensor System Using Neural Networks

2007 ◽  
Author(s):  
Pramod R. Watekar ◽  
Seongmin Ju ◽  
Won-Taek Han
Keyword(s):  
Neural Networks ◽  
Fiber Optic ◽  
Sensor System

Strain and failure sensing by the fiber optic Michelson sensor embedded in composite beam

1996 ◽  
Author(s):  
Chang-Sun Hong ◽  
Il-Bum Kwon ◽  
Chun-Gon Kim
Keyword(s):  
Composite Beam ◽  
Fiber Optic

Fiber-Optic Distributed Vibration Sensing Provides Technique for Detecting Sand Production

2010 ◽  
Author(s):  
Stephen Mullens ◽  
Gareth Lees ◽  
Giles Duvivier
Keyword(s):  
Fiber Optic ◽  
Sand Production ◽  
Vibration Sensing

scholarly journals Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 6 ◽  
Author(s):  
Min Huang ◽  
Zhen Liu
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Evidence Theory ◽  
Fault Prediction ◽  
Swarm Optimization ◽  
Sensing Data ◽  
Vibration Sensing

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model’s ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

Distributed fiber optic vibration sensing with wide dynamic range, high frequency response, and multi-points accurate location

2020 ◽  
Vol 124 ◽  
pp. 105966
Author(s):  
Pengfei Ma ◽  
Zhenshi Sun ◽  
Kun Liu ◽  
Junfeng Jiang ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Frequency Response ◽  
High Frequency ◽  
Fiber Optic ◽  
Dynamic Range ◽  
Wide Dynamic Range ◽  
High Frequency Response ◽  
Vibration Sensing
Sign in / Sign up

Export Citation Format

Share Document