Pipeline Health Monitoring Based On FBG Hoop Strain Sensor

Pipeline transportation of liquids and gas is a vital part of the national economy. As the contents of the fluids inside pipelines are harmful to humans and the environment, there is a priority placed on developing methods for detecting damage, such as leakage, in pipelines. Over the past years, a variety of leakage detection methods have been developed due to the emergence of new materials and sensor technologies. A fast growing portion of these sensor technologies include fiber optic sensors due to their unique benefits. This dissertation presents a new fiber optic based approach to pipeline corrosion monitoring and leakage localization. The approach utilizes fiber Bragg grating (FBG) fiber optic sensors to measure the hoop strain variation along the pipeline due to changes in pipeline wall thickness. The following are the four research directions in this dissertation.(1) Both corrosion and leakage induced depressurization lead to measurable changes in the hoop strain of the pipeline wall. For corrosion, an FEM analysis was carried out that showed measurement of average hoop strain is in general more sensitive than point measurements in detecting local corrosion. For leakage localization, an arrival time difference method was used based on the measurement of negative pressure wave (NPW) induced hoop strain variation of the pipeline due to leakage. For small rate leakages, an energy attenuation method was used. The pipeline corrosion and leakage simulation methods were conducted and verified on steel model and PVC model pipelines respectively.(2) A novel encapsulation method for an FBG hoop strain sensor was developed to meet pipeline hoop strain measurement requirements. This sensor can be used to monitor the average hoop strain and thus evaluate corrosion severity. The sensor can also monitor dynamic hoop strain variation induced by leakage. This research direction investigates the performance of the novel FBG hoop strain sensor, including the sensitivity, stability, etc. and then demonstrates its effectiveness and practicability in pipeline health monitoring. A pre-tensioning mechanism was designed to ensure matching deformation between the sensor and the pipeline.(3) By the method of characteristics, the distribution of hoop strains along the pipeline in the steady state after leakage was analyzed. Combined with a back propagating (BP) neural network, a pipeline leakage localization method based on steady state hoop strain distribution is proposed. In this dissertation, the leakage localization accuracy rates of neural networks with different numbers of hoop strain measuring points and hidden layer nodes was analyzed and compared to obtain an optimized network prediction structure.(4) The minimum leakage rate required for detection by the method can be significantly reduced by increasing the number of hoop strain measurement points. In this research direction, an analytical model of the NPW energy attenuation was used to determine the best sensor spacing along a pipeline according to different leakage rate requirements. The influences of several pipeline operating parameters to hoop strain detectable radius were also analyzed.