Performance Optimization of Excessively Tilted Fiber Grating Cantilever Beam Vibration Sensor

2019 ◽  
Vol 39 (8) ◽  
pp. 0806006
Author(s):  
罗彬彬 Luo Binbin ◽  
谢浪 Xie Lang ◽  
王亚杰 Wang Yajie ◽  
邹雪 Zou Xue ◽  
石胜辉 Shi Shenghui ◽  
...  
Keyword(s):  
Cantilever Beam ◽  
Performance Optimization ◽  
Vibration Sensor ◽  
Fiber Grating ◽  
Beam Vibration

A fiber cantilever beam vibration sensor with air cavity based on intensity modulation

2021 ◽  
Vol 67 ◽  
pp. 102732
Author(s):  
Chang Liu ◽  
Yanyan Chu ◽  
Xinghu Fu ◽  
Wa Jin ◽  
Guangwei Fu ◽  
...  
Keyword(s):  
Cantilever Beam ◽  
Intensity Modulation ◽  
Vibration Sensor ◽  
Air Cavity ◽  
Beam Vibration

A two-dimensional cantilever beam vibration sensor based on fiber Bragg Grating

2021 ◽  
Vol 61 ◽  
pp. 102447
Author(s):  
Zhen'an Jia ◽  
Xianfeng Zhao ◽  
Wei Fan ◽  
Hong Gao ◽  
Qinpeng Liu ◽  
...  
Keyword(s):  
Fiber Bragg Grating ◽  
Cantilever Beam ◽  
Vibration Sensor ◽  
Bragg Grating ◽  
Two Dimensional ◽  
Beam Vibration

Broadband Vibration Sensor Using Reflected Excessively Tilted Fiber Grating With Clamped Beam

2021 ◽  
Vol 33 (8) ◽  
pp. 379-382
Author(s):  
Kaijun Liu ◽  
Binbin Luo ◽  
Decao Wu ◽  
Xue Zou ◽  
Xianglong Zou ◽  
...  
Keyword(s):  
Vibration Sensor ◽  
Fiber Grating

A fiber grating dual-parameter sensor of ditrigon cantilever beam for measurement of strain and temperature

2007 ◽  
Author(s):  
Jinlong Zhang ◽  
Chongxiu Yu ◽  
Kuiru Wang ◽  
Cheng Li ◽  
Junying Zeng
Keyword(s):  
Cantilever Beam ◽  
Fiber Grating

Highly sensitive operation of intensity-based fiber-optic vibration sensor using cascaded long period fiber grating

2014 ◽  
Author(s):  
Satoshi Tanaka ◽  
Osamu Tsukida ◽  
Makoto Takeuchi ◽  
Shingo Tekuramori ◽  
Ryotaro Uchimura ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Vibration Sensor ◽  
Fiber Grating ◽  
Long Period Fiber Grating ◽  
Long Period ◽  
Highly Sensitive ◽  
Period Fiber Grating

Study on vibration sensing performance of an equal strength cantilever beam based on an excessively tilted fiber grating

2018 ◽  
Vol 57 (9) ◽  
pp. 2128 ◽  
Author(s):  
Binbin Luo ◽  
Wanmeng Yang ◽  
Xinyu Hu ◽  
Huafeng Lu ◽  
Shenghui Shi ◽  
...  
Keyword(s):  
Cantilever Beam ◽  
Fiber Grating ◽  
Equal Strength ◽  
Vibration Sensing ◽  
Sensing Performance

Performance Optimization Two-Node Support Structure of Free Beam Vibration Gyro

2013 ◽  
Vol 645 ◽  
pp. 476-481 ◽  
Author(s):  
Yu Liu ◽  
Shen Liu ◽  
Wen Ji Xiong ◽  
Hong Ming Zeng
Keyword(s):  
Performance Optimization ◽  
Optimization Design ◽  
Model Simulation ◽  
System Optimization ◽  
Design Parameters ◽  
Driving Frequency ◽  
Support Structure ◽  
Beam Vibration ◽  
Shock Resistance ◽  
Free Beam

Establishing the system model of free beam vibration gyro under the influence of the Support structure, deriving and analyzing its vibration performance, comparison between model simulation through ANSYS software and relative theoretical calculation indicates that the relative error is less than 0.25%. Researches on the relations among system sensitivity, bandwidth, shock resistance and anti-interference ability of its model and sizes of Support structure contribute to principles of system optimization design and a set of design parameters: free beam (50mm×4.1mm×4.12mm), cylindrical Support structure(radius0.25mm×2.46mm), the performance of model is as: system driving frequency (8589.8HZ), bandwidth (39.4HZ), shock resistance (411.02g) and anti-interference frequency (above2381.3HZ).

FBG Intrusion Recognition Algorithm Based on SVM

2012 ◽  
Vol 591-593 ◽  
pp. 1422-1427 ◽  
Author(s):  
Jian Wang
Keyword(s):  
Optical Fiber ◽  
Frequency Band ◽  
Time Domain ◽  
Recognition Algorithm ◽  
Vibration Sensor ◽  
Weighting Coefficient ◽  
Support Vector ◽  
Fiber Grating ◽  
University Of Technology

The perimeter security alarm system, with the core of vibration sensor based on FBG optical fiber invented by Wuhan University of Technology optical fiber research center, uses the changes of optical fiber grating stress wavelength to detect intrusion. As FBG wavelengths change when under stress, the intrusion detection is identified by the combination of signals time domain and frequency band domain. However, it is difficult to classify the data of signals time domain and frequency band domain because of the complexity of the actual conditions and the diversity of interference and intrusion. Therefore, applying SVM to train and process the classification features extracted from the various data acquisition can help to obtain the corresponding support vector and weighting coefficient. As a result, the effective classification of the field test data can be achieved by using SVM template method.

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