A light intensity monitoring method based on fiber Bragg grating sensing technology and BP neural network

2017 ◽  
Author(s):  
Lu-Ming Li ◽  
Qian Zhu ◽  
Zhi-Guo Zhang ◽  
Zhi-Min Cai ◽  
Zhi-Jun Liao ◽  
...  
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Fiber Bragg Grating ◽  
Bp Neural Network ◽  
Bragg Grating ◽  
Monitoring Method ◽  
Sensing Technology

Mobile-distributed seepage monitoring method based on fiber Bragg grating sensing technology

Sensor Review ◽  
2018 ◽  
Vol 38 (3) ◽  
pp. 345-352
Author(s):  
Jiang Chen ◽  
Junli Zheng ◽  
Feng Xiong
Keyword(s):  
Fiber Bragg Grating ◽  
Monitoring System ◽  
Spatial Resolution ◽  
Bragg Grating ◽  
Monitoring Methods ◽  
Seepage Velocity ◽  
Monitoring Method ◽  
Content Type ◽  
Sensing Technology ◽  
Tube Structure

PurposeThe spatial resolution of seepage monitoring methods based on fiber Bragg grating (FBG) temperature sensing technology is limited by the distance between measurement points. Improving the spatial resolution for a given number of measurement points is a prerequisite for popularizing this technology in the seepage monitoring of rockfill dams. The purpose of this paper is to address this problem.Design/methodology/approachThis paper proposes a mobile-distributed seepage monitoring method based on the FBG-hydrothermal cycling seepage monitoring system. In this method, the positions of the measurement points are changed by freely dragging the FBG sensing cluster within the inner tube of a dual-tube structure, consisting of an inner polytetrafluoroethylene tube and outer polyethylene of raised temperature resistance heating tube.FindingsA seepage velocity calibration test was carried out using the improved monitoring system. The results showed that under a constant seepage velocity, the use of the dual-tube structure enables faster cooling, and the cooling rate accelerates with an increase in the diameter of the inner tube. The use of the dual-tube structure can improve the sensitivity of the seepage evaluation indexζvto the seepage velocity. When the inner diameter increases,ζvbecomes more sensitive to the seepage velocity.Originality/valueA mobile-distributed seepage monitoring method based on FBG sensing technology is proposed in which the FBG sensors are not fixed. Instead, the positions of the measurement points are changed to improve the spatial resolution. Meanwhile, the use of the dual-tube structure in the presented monitoring system can improve its sensitivity.

Fiber Bragg grating temperature calibration based on BP neural network

Optik ◽  
2018 ◽  
Vol 172 ◽  
pp. 753-759 ◽  
Author(s):  
Yang An ◽  
Xiaocen Wang ◽  
Zhigang Qu ◽  
Tao Liao ◽  
Zhongliang Nan
Keyword(s):  
Neural Network ◽  
Fiber Bragg Grating ◽  
Bp Neural Network ◽  
Bragg Grating ◽  
Temperature Calibration

scholarly journals Slope monitoring and stability analysis of open pit mine based on fiber Bragg grating sensing technology

2018 ◽  
Vol 1065 ◽  
pp. 252002 ◽  
Author(s):  
Ligang Wang ◽  
Lewen Yu ◽  
Yuansheng Zhang ◽  
Da Zhang ◽  
Zhigang Tao ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Fiber Bragg Grating ◽  
Open Pit Mine ◽  
Open Pit ◽  
Bragg Grating ◽  
Sensing Technology ◽  
Slope Monitoring

scholarly journals Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1385
Author(s):  
Sheng Wu ◽  
Kwok L. Lo
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Bp Neural Network ◽  
Distribution Curve ◽  
Electrical Characteristics ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Current Frequency ◽  
Operation Pattern ◽  
Load Monitoring

Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

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