Pipelines function as blood vessels serving to bring life-necessities, so their safe usage is one of the foremost concerns. In our previous work, a fiber Bragg grating (FBG) hoop strain sensor with enhanced sensitivity was developed to measure the pressure drop induced by pipeline leakage. Some hoop strain information during the leakage transient process can be extracted from the amount of FBG hoop strain sensors set along the pipeline. In this paper, an integrated approach of a back-propagation (BP) neural network and hoop strain measurement is first proposed to locate the leak points of the pipeline. Five hoop strain variations are employed as input neurons to achieve pattern recognition so as to predict the leakage point. The RMS error can be as low as 1.01% when choosing appropriate hidden layer neurons. Furthermore, the influence of noise on the network’s performance is investigated through superimposing Gaussian noise with a different level. The results demonstrate the feasibility and robustness of the neural network for pipeline leakage localization.

Pipeline leakage and corrosion are two serious threats to pipeline safety operation. Therefore, to ensure the safety operation of long-distance pipeline, it is of great significance to conduct pipeline monitoring. Since hoop strain is an effective indicator to reflect the inner pressure fluctuation and the wall thickness reduction of the pipeline, a method of monitoring leakage and corrosion simultaneously was proposed based on hoop strain measurement. In order to test the hoop strain variation, this paper introduces a fiber Bragg grating (FBG) strain hoop sensor. To verify the monitoring method and the performance of this FBG strain sensor, a pipeline leakage simulation experiment and corrosion simulation experiment were conducted on an actual pipeline and a polyvinyl chloride (PVC) pipe model, respectively. The experimental results demonstrate that the pipeline leakage and corrosion can be detected by the FBG hoop strain sensor. The FBG strain hoop sensor is a promising device in pipeline safety monitoring.

Pipeline leakage and pipeline internal corrosion are two serious threats to pipeline safety operation. In this study, based on the measurement theory of pipeline internal diameter variation and a numerical simulation analysis of existing sensor shortcomings, a novel FBG pipe-fixture sensor is designed and developed to detect pipeline leakage and monitor pipeline internal corrosion simultaneously. A calibration test is conducted, and the test results indicate that the FBG pipe-fixture sensor solves the problem of a large difference between the test values and the theoretical values and eliminates the limitation of measurement range. To validate the actual performance of the sensor, a pipeline leak detection test and a pipeline internal corrosion monitoring test are, respectively, conducted on a steel pipe leak test platform and a steel pipe with different simulated corrosion levels. The test results demonstrate that the FBG pipe-fixture sensor can effectively detect pipe leakage and decrease the localization error of the leakage point to within 1 m. The sensor can also effectively monitor pipe internal corrosion and evaluate the internal corrosion degree with a measurement accuracy of 0.1 mm. The novel FBG pipe-fixture sensor may be a potential sensor for pipeline leak detection and pipeline internal corrosion monitoring.