Pipeline Detection Research Articles

Construction and experimental verification research of a magnetic detection system for submarine pipelines based on a two-part towed platform

With the acceleration of the investigation and development of marine resources, the detection and location of submarine pipelines have become a necessary part of modern marine engineering. Submarine pipelines are a typical weak magnetic anomaly target, and their magnetic anomaly detection can only be realized within a certain distance. At present, a towfish or an autonomous underwater vehicle (AUV) is mainly used as the platform to equip magnetometers close to the submarine pipelines for magnetic anomaly detection. However, the mother ship directly affects the towfish, thus causing control interference. The AUV cannot detect in real time, which affects the magnetic anomaly detection and creates problems regarding detection efficiency. Meanwhile, a two-part towed platform has convenient control, thus reducing the interference of the towed mother ship and real-time detection. If the platform can maintain constant altitude sailing through the controller, the data accuracy in the actual magnetic anomaly detection can be guaranteed. On the basis of a two-part towed platform, a magnetic detection system with constant altitude sailing ability for submarine pipelines was constructed in this study. In addition, experimental verification was conducted. The experimental verification research shows that the constant altitude sailing experiment of the two-part towed platform verifies that the platform has good constant altitude sailing ability in both a hydrostatic environment and the actual marine environment. Meanwhile, the offshore magnetic anomaly detection experiment of submarine pipelines verifies the stable measurement function of the magnetic field and the function of the system to detect magnetic anomaly of submarine pipelines.

Gas pipeline safety management system based on neural network

AbstractThe risk of leakage poses a grave threat to natural gas pipeline safety. The high compressibility of gases combined with unsteady boundary conditions makes detecting leaks in pipelines a challenging endeavor. To date, in the literature, only a limited number of studies have focused on leak detection and diagnostics in gas mixture pipelines. The present study provides a system for detecting, locating, and estimating the size of small gas leaks from a compressible and dynamic natural gas flow in pipelines with improved accuracy. As a case study, a long natural gas pipeline of 80 km is simulated with leak sizes of 0%, 2%, and 5%. The safety system is developed using mass flow rate, temperature, and pressure measurements. Six classes for faulty cases and one class for no fault case were considered for the study. A shallow neural network classifier (SNNC) is trained to identify a specific fault class. The SNNC is based on a two‐layered network with 20 and 7 neurons. An input vector of 15 variables is provided to the system, and the output is one of the seven possible classes. Leakage as low as 2% at various locations are correctly diagnosed with more than 99% correct classification rate.

DL-Based Clutter Removal in Migrated GPR Data for Detection of Buried Target

As a nondestructive and nonintrusive geophysical electromagnetic technology, ground-penetrating radar (GPR) has been widely applied to subsurface target detection, such as landmine detection, pipeline detection, and underground cavity detection. The target response received by the GPR system is generally contaminated by clutter, which greatly affects the detection performance of the buried targets. In this letter, a novel clutter removal method combining migration and dictionary learning (DL) is presented. First, the proposed method applies the frequency–wavenumber (F–K) migration to the received GPR B-scan data. Then, since the focused target response and the clutter in the migrated GPR B-scan data present different morphological components, DL can be applied to the migrated GPR B-scan data to separate the focused target response (point-shaped structure) from the clutter (horizontal strip-shaped structure). Both numerical simulated data and experimental data collected by a real GPR system are used to evaluate the performance of the proposed clutter removal method. The experimental results demonstrate the effectiveness of the proposed clutter removal method under irregular clutter conditions, which improves the detection ability of the buried targets.

Pipeline Leak Detection Using Acoustic Emission and State Estimate in Feature Space

Existing acoustic emission signal–based methods for pipeline leak detection usually denoise the raw signals directly in signal–space, then extract signatures from denoised signals, and finally classify normal/leaky states via classifiers trained using offline datasets. Their complex computational structures may limit their real–time application, especially, when they will be required to analyze massive amounts of data. Furthermore, these methods may not be effective in leak detection in real pipelines where acoustic emission signals might be prone to constant fluctuation. This paper proposes a novel technique to mitigate these issues. It combines a Kalman filter and an outlier removal technique to estimate the true state in feature–space and identifies a leak through normalized distance from an unknown class to a well–known class with a threshold. The experimental results show that the proposed method achieves an average true detection rate of 96.9% and an average omission rate of 3.6% compared to existing methods, which achieve a maximum average true detection rate of 92% and a minimum average omission rate of 8.8%. Moreover, the proposed method can achieve these results in real time.

Detection of oil pipelines’ heat loss via machine learning methods

In cold climates, when transporting oil with a high paraffin content, oil is heated up to 50 °С. The large surface area, insulation quality and installation conditions also influence heat loss from the main pipeline. Heat losses outcome in warming of the soil and changes in the spatial position of buried oil pipelines. As a result, pipeline operators construct geotechnical networks to measure soil temperature at different depths and to determine spatial changes along the route. Despite the large amount of temperature readings, pipeline operators do not employ sufficient machine learning techniques to detect heat losses. The paper offers an approach that includes generating multidimensional dataset, excluding omissions and outliers, grouping the data by wells, balancing the number of measurements in the groups, performing an upward sampling, training the auto-regressive additive model and separating the trend component from the seasonal one.

Experimental Investigation for Multi-Defect Detection of Industrial Pipeline Based on Passive Infrared Thermal Imaging Analysis: A Case Study

Experimental Investigation for Multi-Defect Detection of Industrial Pipeline Based on Passive Infrared Thermal Imaging Analysis: A Case Study

Condition detection of pipeline holding in mine shafts by the corrosive wear criterion

Condition detection of pipeline holding in mine shafts by the corrosive wear criterion

Detection and Localization of Buried Pipelines Using a 3-D Multistatic Imaging Radar

Subsurface pipeline detection and localization is an important problem in gas and oil transport as well as in construction and excavation missions. A 3-D subsurface multistatic imaging radar with a novel focusing algorithm for pipelines is demonstrated in this article. The traditional back-projection (BP) algorithm, which is suitable for discrete scatterers, cannot be used for extended scatterers such as pipelines whose scattering phase centers are dependent on the relative positions of the transmitter and receiver. To circumvent this problem, a new imaging algorithm that tracks the scattering phase centers in a way that is specific to pipelines is presented and shown to provide significantly better imaging performance. An analytical solution for pipeline scattering is used to derive the algorithm, and justifications for the simplifying assumptions made are provided. The algorithm is tested by applying it to realistic lossy sand simulations data as well as experimental measurements data obtained by a portable vector network analyzer (VNA). Both simulation and measurement results demonstrate the ability of the algorithm to detect and localize metallic as well as dielectric pipes.

Leakage detection in underwater oil and natural gas pipelines using convolutional neural networks

Underwater oil and natural gas pipelines are an underwater transport infrastructure known to be reliable, fast, and efficient, preferred for the transmission of energy to far distances. The rapid and continuous increase in demand for energy due to population growth, industrial developments, and global growth requires economic and environmental solutions for the safe transmission and control of energy sources such as oil and natural gas. These lines are damaged due to their work in corrosive ambient conditions, natural elements such as sudden change of air and water temperatures, tectonic activities, and external elements such as blows caused by fishing equipment and military exercises. Therefore, it is necessary to determine the damages without requiring more hardware, saving time, and cost. In this study, underwater oil and natural gas pipelines were detected using convolutional neural networks and the detection performance of artificial neural network was analyzed. Underwater pipelines are detected using convolutional neural networks with 97.63% accuracy. A reliable, fast, efficient, controlled, and sustainable model is established to prevent potential damage to underwater pipelines from becoming an environmental threat to water and air pollution and living creatures in the underwater ecosystem with this study.

Leak detection and localization for pipelines using multivariable fuzzy learning backstepping

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups.

Guided Wave Transducer for the Locating Defect of the Steel Pipe Based on the Weidemann Effect

The electromagnetic guided wave transducer has been widely used in pipeline detection in recent years due to its non-contact energy conversion characteristics. Based on the Weidemann effect, an electromagnetic guided wave transducer that can realize the locating defect of the steel pipe was provided. Firstly, the principle of the transducer was analyzed based on the Weidemann effect. The basic structure of the transducer and the basic functions of each part were given. Secondly, the key structural parameters of the transducer were studied. Based on the size of the magnets and the coils, a protype electromagnetic guided wave transducer based on Wiedemann effect was developed. Finally, the experiments were carried out on the steel pipe with a defect using the developed transducer. The results show that the transducer can actuate and receive the T(0,1) and T(1,1) modes in the steel pipe. The axial positioning of the defect is located by moving the transducer axially. The circumferential positioning of the defect is located by rotating the transducer. Additionally, missed detection can be effectively avoided by rotating the transducer.

Risks and critical success factors in the internationalization of born global startups of industry 4.0: A social, environmental, economic, and institutional analysis

Startups in the Brazilian organizational scenario have been growing fast and understanding these startups' internationalization strategies have been highly relevant. Such startups have developed a social, environmental, and economic impact solution that had the support of government programs in its internationalization process, seeking to understand the impact of these programs. This article has sought the following: (1) identify the risks and critical success factors in the internationalization process of born global startups of industry 4.0, (2) analyze the social, environmental, and economic aspects. The authors have based the methodology on a single-case analysis, a global startup - Asel-Tech, a technology and automation company focused on developing and providing solutions for leak detection in oil pipelines and derivatives. Among the main results, we highlight: (1) the proposition of a model for risk and critical success factors analysis in the internationalization of born global startups of industry 4.0, (2) the analysis from the institutional view perspective on the strategy, on the social, environmental, and economic aspects of digital manufacturing. For professionals, the article provides a model to assist startups in future internationalization processes.

A novel noise reduction method for natural gas pipeline defect detection signals

Since pipeline defect detection signals are often contaminated by noise, the accuracy of defect identification is reduced if inappropriate noise reduction methods are used. Therefore, the noise reduction of pipeline detection signals is critical for pipeline defect detection preprocessing technology. A denoising method based on differential-based empirical mode decomposition (DEMD) and extremum field mean mode decomposition (EMMD) is proposed in this paper and named derivative extremum field mean mode decomposition (DEMMD). The idea of DEMMD is to replace the EMD part of DEMD with EMMD. To simultaneously reduce the interference of noise and retain useful signal features, intrinsic mode function (IMF) reconstruction is viewed as a two-objective optimization problem. The objective function is constructed by a similarity measure based on a probability density function and a complexity measure based on fuzzy entropy. Monte Carlo simulations are performed to simulate the possible IMF component combinations. Then, based on the idea of a Pareto optimal solution, the IMF components that satisfy the optimization objective are selected to construct the denoised signal. Synthetic signal and field test data are used to verify the proposed method. The denoising results of the synthetic signal show that the method can decompose the main characteristic modes of defect signals and effectively recover the original signal. The results of a case study show that the denoised signal obtained by this method can not only suppress noise but also retain the defect details during reconstruction. The signal-to-noise ratio (SNR) and root mean square error (RMSE) are used to measure and compare the performance of the proposed method. The results show that the SNR of the proposed method is the highest, which is 29.1207, and the RSME is the lowest, which is 0.0045. Compared with other traditional denoised methods, it outperforms and suits pipeline detection signal denoising.

Discussion on Application of Integrated Geophysical Technique in Underground Pipeline Detection

Discussion on Application of Integrated Geophysical Technique in Underground Pipeline Detection

Magnetic anomaly detection of adjacent parallel pipelines using deep learning neural networks

Magnetic anomaly detection is becoming increasingly prevalent for detecting and locating the buried pipelines. The detection performance is often hindered by adjacent pipeline, near-field ferromagnetic objects and random noises. In order to overcome these obstacles, a magnetic anomaly detection method based on deep learning neural networks (DLNN) is proposed to decouple and denoise the integrated detection to accurately extract the magnetic anomaly of single pipeline. The theoretical derivation of the vertical component of magnetic anomaly was executed based on Poisson's equation. Then the integrated detection was simulated by summing magnetic anomalies of parallel pipelines and metal sphere, as well as white Gaussian noise. The DLNN was constructed with improved optimization design, and trained using supervised learning method. The results show that the proposed method exhibits almost immune to random noises, the prediction accuracy approaches to 90% with signal to noise ratio (SNR) of 30 dB. Meanwhile, the predictive accuracy is still above 80% with interferences both from near-field ferromagnetic objects and random noises with SNR of 30 dB. The method becomes practically significant in the development of geomagnetic inspection instruments for the adjacent parallel pipelines.

Real-time embedded system for valve detection in water pipelines

Real-time embedded system for valve detection in water pipelines

Omnidirectional ghost imaging system and unwrapping-free panoramic ghost imaging.

Ghost imaging (GI) is an unconventional imaging method that reconstructs the object information via light-intensity correlation measurements. However, at present, the field of view (FOV) of this method is limited to the illumination range of light patterns. To enlarge the FOV of GI efficiently, we propose an omnidirectional GI system (OGIS) that can achieve a 360° omnidirectional FOV only via the addition of a curved mirror. The OGIS features retina-like annular patterns designed as a log-polar structure and can obtain the undistorted unwrapping-free panoramic images with uniform resolution. This research presents a new, to the best of our knowledge, perspective for the applications of GI, such as pipeline detection, a panoramic situation awareness for autonomous vehicles.

Current technologies and the applications of data analytics for crude oil leak detection in surface pipelines

Pipeline pressure monitoring has been the traditional and most popular leak detection approach, however, the delays with leak detection and localization coupled with the large number of false alarms led to the development of other sensor-based detection technologies. The Real Time Transient Model (RTTM) currently has the best performance metric, but it requires collection and analysis of large data volume which, in turn, has an impact in the detection speed. Several data mining (DM) methods have been used for leak detection algorithm development with each having its own advantages and shortcomings. Mathematical modelling is used for the generation of simulation data and this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the ANN and SVM require a large training dataset for development of accurate models, mathematical modelling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modelling for the development of a robust real time leak detection and localization system for surface pipelines. Several case studies are also presented.

Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles

With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. However, DL algorithms require massive representative samples, which are difficult to obtain for pipeline detection with sub-bottom profiler (SBP) data. In this paper, a zero-shot pipeline detection method is proposed. First, an efficient sample synthesis method based on SBP imaging principles is proposed to generate samples. Then, the generated samples are used to train the YOLOv5s network and a pipeline detection strategy is developed to meet the real-time requirements. Finally, the trained model is tested with the measured data. In the experiment, the trained model achieved a mAP@0.5 of 0.962, and the mean deviation of the predicted pipeline position is 0.23 pixels with a standard deviation of 1.94 pixels in the horizontal direction and 0.34 pixels with a standard deviation of 2.69 pixels in the vertical direction. In addition, the object detection speed also met the real-time requirements. The above results show that the proposed method has the potential to completely replace the manual interpretation and has very high application value.

Control Strategy of Pipeline Robot

The detection of pipeline in service is very important, but the existing manual detection has blind spots, long cycle, high risk, low precision and high cost. Therefore, it is very important to develop a mobile detection robot to replace manual inspection. This paper mainly introduces the control strategy of pipeline robot. In this paper, the overall control structure of the pipeline robot system is built, including the design of hardware framework and the design of software implementation process. Hardware design includes the selection design of microcontroller ARM, sensor, motor driver and so on. The software design is mainly the process design of action realization and the frame design of man-machine interactive operation panel structure. After the system construction is completed, the motion control experiment of the pipeline robot control system is carried out to test the walking performance of the pipeline robot prototype in the vertical variable diameter pipeline, and the motion control experiment is carried out to verify the rationality of the mechanical structure of the pipeline robot and the practicability of the control system.