Study on Leak Detecting System for Oil Pipeline Based on Multi-Sensor Data Fusion Technique

2012 ◽  
Vol 220-223 ◽  
pp. 1898-1901
Author(s):  
Jie Zhang ◽  
Xiao Wang ◽  
Zhen Zhao

In this paper, based on the wavelet transform method, the multi-sensor data fusion technology is adopted to solve some key problems in pipeline leak detection system. Compared with the traditional filtering method, the wavelet transform method can better remove the pipeline leakage signal noise. When the corresponding wavelet is selected, after FPGA realized, the method has the advantages of fast speed, arbitrarily data width set, which can better meet the needs of real-time signal processing requirements of pipeline leak. At the same time, VHDL language has the characteristics of portability, greater generality. When multi-sensor fusion technology is used in the paper, the relation, correlation, estimation and integrated processing is done in turn to the information from multiple data sources, the system can achieve better detection performance than a system using the single sensor, so as to form a more complete, reliable pipeline real time detecting conclusion.

2013 ◽  
Vol 721 ◽  
pp. 479-482 ◽  
Author(s):  
Yan Xia Wang ◽  
Chun Hui Bao ◽  
Chun Ling Fan

The multi-sensor data fusion techniques is discussed in dynamic weighing system based on the data measured from ultrasonic sensors, speed sensors, capacitive sensors and load cells. This new method can greatly increase the measure precision of the dynamic weighing systems, at the same time it can effectively reduce noise, vibration, electromagnetic interference and other environmental factors on the influence of dynamic weighing system measurement. Judging from the simulation result, this new method proves to be more accurate and stable than ordinary processing methods.


Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 919 ◽  
Author(s):  
Hao Du ◽  
Wei Wang ◽  
Chaowen Xu ◽  
Ran Xiao ◽  
Changyin Sun

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.


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