Corrosion Detection System for Oil Pipelines Based on Multi-sensor Data Fusion by Wavelet Neural Network
Title
Corrosion Detection System for Oil Pipelines Based on Multi-sensor Data Fusion by Wavelet Neural Network
Subject
Corrosion
corrosion detection
Leak detection
multisensor data fusion
Neural networks
oil pipeline
Petroleum
Pipelines
Sensor phenomena and characterization
Sensor systems
Underwater vehicles
Wavelet analysis
Wavelet domain
wavelet neural network
Description
A system to detect the corrosion of submarine oil pipeline is introduced, it got the original data by 3 groups ultrasonic sensors and flux leakage sensors. We made multiscale wavelet transform and frequency analysis to multichannels original data and extracted multi-attribute parameters from time domain and frequency domain, then we selected the key attribute parameters that have bigger correlativity with the corrosion degrees of oil pipeline among of multi-attribute parameters. The wavelet neural network was used to do multisensor data fusion to detect the corrosion degrees of submarine oil transportation pipelines and those key attribute parameters were used to as input vectors of network. The experimental results show that this method is feasible and effective.
2958-2963
Creator
J. Tian
M. Gao
H. Zhou
K. Li
Publisher
2007 IEEE International Conference on Control and Automation
Date
2007
Type
conferencePaper
Identifier
1948-3457
Collection
Citation
J. Tian et al., “Corrosion Detection System for Oil Pipelines Based on Multi-sensor Data Fusion by Wavelet Neural Network,” Lamar University Midstream Center Research, accessed May 14, 2024, https://lumc.omeka.net/items/show/27489.