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.

Pipeline, serving as one of the most important gas transportation methods, can cause serious consequences in the event of leakage, so leakage monitoring is particularly important. In our previous study, a fiber Bragg grating hoop strain sensor was developed to detect pipeline leakage by measuring circumferential strain, and this sensor was utilized in a liquid pipeline leakage test to verify its performance. In this article, we established a full-scale gas pipeline model and investigated the performance of this fiber Bragg grating hoop strain sensor in leakage monitoring. On the basis of circumferential strain variation characteristic before and after negative pressure wave arrival at a fiber Bragg grating hoop strain sensor, a linear fitting algorithm combined with threshold detection was proposed to capture the knee point of circumferential strain. Test results illustrated that the proposed approach has good performance in knee point detection, and fiber Bragg grating hoop strain sensor is suitable for gas pipeline leakage monitoring.

This paper proposes a multi-radial-distance event classification method based on deep learning. To the best of our knowledge, this is the first time that the $\Phi $ -OTDR can tell how far the target event from the sensing fiber is through deep learning approach. The temporal-spatial data matrix collected by the system is filtered by three different band-pass filters to form RGB images as the input of the Inception_V3 network trained by ImageNet dataset. The passband of three band-pass filters is selected by searching the maximum Euclidean distance in the frequency domain. Three kinds of filters with different frequency bands enhance the effective features of data samples in advance. The simulated annealing (SA) algorithm is applied to search the maximum Euclidean distance. Field experiment includes five kinds of events with four different radial distances, where there are 17 subclasses in total, has been carried out. The classification results show that the classification accuracy reaches 86% and the method can tell both the event type and radial distance.

Ultrasonic nondestructive testing technique has been widely applied to rail inspection. Due to the influence of probe placement and the complex structure of the rail, current devices are limited in the ability of detecting most defects existing in the rail base. In this paper, the electromagnetic acoustic transducer (EMAT) is applied to the inspection of rail base defects. Wave modes in the rail base and their corresponding mode shapes are obtained by applying the finite element analysis (FEA) method. The analysis result implies that pseudo-Rayleigh waves have the potential to detect concerned defects in the frequency range of 300 to 400kHz. To further test the inspection ability of pseudo-Rayleigh waves, an EMAT generating 300kHz pseudo-Rayleigh waves is designed and two artificial defects of different types in a rail base specimen are detected using this EMAT. The experiment result indicates that pseudo-Rayleigh waves are suitable for the inspection of rail base.

Pipelines provide an economical mode of transportation for hauling gas and liquids such as oil over long distances. Pipeline explosions cause massive loss of lives, and adversely impact both the environment and the economy. Accurate and automated detection of the arrival times of negative pressure waves (NPW) because of leakages is the key parameter in many pipeline leakage detection approaches. The method introduced here uses the absolute maximum difference in the slopes of NPW time-domain signals for accurate determination of the time of signal arrivals at the sensors. The proposed method involves Fiber Bragg Grating (FBG) sensor-based detection of the leakages, and determination of the arrival of NPW signals for accurate detection of the knee-points in the signals. The main advantage in using the proposed approach is determination of exact arrival time of NPW for accurate detection of leakage point locations along the lengths of pipelines. The experimental program includes construction of two separate gas and liquid pipelines for evaluation of the absolute maximum slope difference (AMSD) method. The study includes evaluation of the differences between the proposed method and conventional visual determination of the NPW arrival times, and the actual locations of the leakage sources in the experiments. The numerical approach introduced herein enables direct integration of the proposed method, for automated real-time monitoring of pipeline leakage locations.