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Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to t...
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... resonant signal is eventually emitted by the wall to the transducer and is detected as tone bursts of smaller amplitudes, compared to those of the ultrasound signal that was reflected earlier by the wall. Figure 2 summarises the operating principle of ART. By measuring the amplitudes of the resonance and their respective frequencies for different thicknesses of the wall of a pipeline as well as their respective frequencies, the thickness of the wall for a random pipeline can be derived using data interpolation with estimated accuracies of ±0.2 mm. ...
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... stochastic tool, based on the successive linear estimator (SLE) proposed in [80] was able to provide a good estimate of the length and the size of the blockage in a single, short duration transient test, by measuring the pressure at different sections of a pipeline using piezoelectric sensors. SLE, despite the presence of many structural errors in the transient simulation, was still able to provide an approximation of the dimensions of the blockage with low relative errors as shown in Figure 20. This was due to the accountability of SLE for the complex geometries of blockages. ...
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... sensors are commonly used in measuring the flow rate of the gas transported in a natural gas pipeline. In [92,94], an in-plane MEMS capactive flow sensor as shown in Figure 22 was designed to measure the gaseous flow velocity. The sensor operates on the basis of employing the displacement of a micro-fabricated paddle caused by the dynamic changes in the gas pressure in a pipeline. ...
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... fields lines can only be induced in metallic materials, which have high magnetic permeability. Figure 23a shows how magnetic flux leakage, caused by a defect on the wall of a pipe, is measured using a Hall effect sensor. A Hall effect sensor outputs a voltage that is proportional to the magnitude of the magnetic field measured. ...
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... order to address the limitation of MFL-based inspection in covering the entire length and surface area of the internal walls of pipelines, the design of a circumferential MFL inspection tool was proposed in [4,16,95]. Figure 23b shows the layout of a circumferential MFL pipe inspection gauge (PIG). A pipeline inspection gauge (PIG) travels in the pipeline to perform specific inspection tasks based on the types of sensors equipped. ...
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... localisation system is suitable for the identification of the locations of defects in real time. However, the cost of deployment is increased as the area coverage of the system increases since there is a need to distribute arrays of ELF sensors as shown in Figure 24. A conventional MFL PIG induces magnetic saturation in the wall of a pipeline to detect the presence of discontinuities, such as leakages and cracks. ...
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... high magnetic saturation increases the detectability of severe cracks and ruptures in the wall of a pipeline, it also reduces the sensitivity of the magnetic flux in the detection of smaller defects, such as gradual thinning, grooves and pittings. The limitation of a MFL PIG in terms of the need to achieve high magnetic saturation is addressed in [102], using an internal corrosion sensor (ICS) as shown in Figure 25 to detect metal loss based on field disturbance. The magnet of an ICS, instead of saturating the wall of the pipeline with magnetic flux, as shown in Figure 23a, is oriented to magnetise the wall using weak magnetic fields, as shown in Figure 26a. ...
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... limitation of a MFL PIG in terms of the need to achieve high magnetic saturation is addressed in [102], using an internal corrosion sensor (ICS) as shown in Figure 25 to detect metal loss based on field disturbance. The magnet of an ICS, instead of saturating the wall of the pipeline with magnetic flux, as shown in Figure 23a, is oriented to magnetise the wall using weak magnetic fields, as shown in Figure 26a. Hall effect sensors are positioned close to the wall to measure small changes in the magnetic field strength, as shown in Figure 26b, caused by the presence of minor defects. ...
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... limitation of a MFL PIG in terms of the need to achieve high magnetic saturation is addressed in [102], using an internal corrosion sensor (ICS) as shown in Figure 25 to detect metal loss based on field disturbance. The magnet of an ICS, instead of saturating the wall of the pipeline with magnetic flux, as shown in Figure 23a, is oriented to magnetise the wall using weak magnetic fields, as shown in Figure 26a. Hall effect sensors are positioned close to the wall to measure small changes in the magnetic field strength, as shown in Figure 26b, caused by the presence of minor defects. ...
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... magnet of an ICS, instead of saturating the wall of the pipeline with magnetic flux, as shown in Figure 23a, is oriented to magnetise the wall using weak magnetic fields, as shown in Figure 26a. Hall effect sensors are positioned close to the wall to measure small changes in the magnetic field strength, as shown in Figure 26b, caused by the presence of minor defects. By plotting the magnetic flux density measurements with respect to the position of the ICS sensor from the defect, the depth and length of a corroded area on the wall of a pipeline can be visualised as shown in Figure 27. ...
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... effect sensors are positioned close to the wall to measure small changes in the magnetic field strength, as shown in Figure 26b, caused by the presence of minor defects. By plotting the magnetic flux density measurements with respect to the position of the ICS sensor from the defect, the depth and length of a corroded area on the wall of a pipeline can be visualised as shown in Figure 27. Based on the results in Figure 27, it can be observed that ICS allows the depth and length of a defect of a pipeline to be estimated directly from the measurements obtained by the Hall effect sensors. ...
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... plotting the magnetic flux density measurements with respect to the position of the ICS sensor from the defect, the depth and length of a corroded area on the wall of a pipeline can be visualised as shown in Figure 27. Based on the results in Figure 27, it can be observed that ICS allows the depth and length of a defect of a pipeline to be estimated directly from the measurements obtained by the Hall effect sensors. The distribution of the Hall effect sensors of the ICS, as shown in Figure 25, allows the ICS to measure the magnetic field strengths of a large area of the wall of a pipeline to map the defect with sub-millimetre accuracies. ...
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... on the results in Figure 27, it can be observed that ICS allows the depth and length of a defect of a pipeline to be estimated directly from the measurements obtained by the Hall effect sensors. The distribution of the Hall effect sensors of the ICS, as shown in Figure 25, allows the ICS to measure the magnetic field strengths of a large area of the wall of a pipeline to map the defect with sub-millimetre accuracies. For conventional MFL PIG, an exhaustive set of experiments have to be implemented to first establish relevant relationships between the amplitudes of the MFL, and the dimensions of the defects because the MFL measurements obtained by the Hall effect sensors do not carry any explicit information about the dimensions of those defects. ...
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... induced eddy currents generate a quick-decaying magnetic field that is detected by the pick-up coil in the form of electric voltage [104]. The pulsed eddy current (PEC) testing, as shown in Figure 28, while employing a similar operating principle, uses electric current pulses with a broad frequency spectrum, allowing the eddy currents to penetrate different depths of the layers of the wall of a pipeline [103]. ...
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... common interrogator units, based on the analysis of Rayleigh scattering, are the optical time-domain reflectometer (OTDR) and optical frequency-domain reflectometer (OFDR). The principle of an OTDR is as shown in Figure 29, where the forward and backward optical paths are separated after which photodetectors are used to measure the backward light intensity. By distinguishing the measured intensity values of the light pulses, the positions corresponding to the optical changes on the optical fibres can be determined. ...
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... position of the crack relative to the positions of fibre rings is as shown in Figure 31b. Based on the measurements recorded in Figure 32b, the tip of the crack grows towards Fibre 1 with significant nearness observed between the 10,000th and 12,000th cycles. The distributed strain spectrum in Figure 32a shows a corresponding gradual change in strain with a significant change recorded at the point when the tip of the crack is in contact with Fibre 1. ...
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... on the measurements recorded in Figure 32b, the tip of the crack grows towards Fibre 1 with significant nearness observed between the 10,000th and 12,000th cycles. The distributed strain spectrum in Figure 32a shows a corresponding gradual change in strain with a significant change recorded at the point when the tip of the crack is in contact with Fibre 1. This result provides evidence that DOFS can be used to detect the early onset of cracks, which is useful for preventive and predictive maintenance. ...
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... weakness in the MIT approach in terms of the sensitivity towards smaller metal loss defects was addressed in [125], which proposed the design and fabrication of an ultrasonic tomographic instrumentation system, as shown in Figure 42. A total of 28 ultrasonic transducers were mounted on the sensing ring at a distance of 50 mm from the the external surface of the pipeline segment. ...
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... mounting setup allowed each of the transducers to cover up to 20 mm of the circumference of the pipeline. Each of the transducers in the setup in Figure 42 was responsible for transmitting an incident ultrasonic wave towards the wall of the pipeline segment. The voltage of the reflected ultrasonic wave was then measured as a proxy for the thickness of the pipe wall corresponding to the location of the transducer. ...
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... gamma-ray transmission technique can also be used for the evaluation of the size of a blockage or deposit in a pipeline [137]. Gamma-ray transducers consisting of a gamma-ray source and a detector were fitted outside a pipeline diametrically as shown in Figure 52 to measure the transmitted intensity of the gamma ray across different sections along the length of the pipeline. While the computational strategy of this method that relates the the thickness of deposit on the wall of a pipeline to the intensity of transmitted gamma ray is relatively straightforward, the deployability of the sensing system based on gamma ray transmission is off limits for pipelines in the public environment since the use of gamma radiation for inspection may pose a safety hazard. ...
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... solution, known as TriopusNet, for automating the deployment and replacement of the sensor nodes in a wireless sensor network was proposed in [158] with the aim to reduce the number of sensor nodes required to cover a unit sensing area in a pipeline network without compromising on the state of the network connectivity among the nodes, which collect data at high rates. The TriopusNet node prototype as illustrated in Figure 62 consists of a sensing unit comprising of a pressure sensor and gyroscope and a mechanical latching unit consisting of three mechanical arms. The latching unit allows the sensor node to latch itself to the internal wall of a pipeline. ...
Citations
... While low-frequency guided waves may not be very sensitive to small defects, existing guided wave techniques often struggle to detect the responses generated by local small defects or in cases where the wall is smoothly thinned. This limitation can lead to the oversight and under-detection of defects, highlighting a challenge in current guided wave inspection capabilities [63][64][65][66]. ...
... While low-frequency guided waves may not be very sensitive to small defects, in cases of locally small defects or wall thinning with smooth surfaces, existing guided wave technologies often struggle to detect the responses generated by defects. This limitation can lead to missed detections and new leaks, presenting challenges in current guided wave detection [63][64][65][66]. ...
This paper presents research on the application of ultrasonic-guided wave technology in corrosion defect identification, expounds the relevant ultrasonic-guided wave theories and the principle of ultrasonic-guided wave non-destructive testing of pipelines, and discusses the Lamb wave and shear horizontal wave mode selection that is commonly used in ultrasonic-guided wave corrosion detection. Furthermore, research progress in the field of ultrasonic-guided wave non-destructive testing (NDT) technology, i.e., regarding transducers, structural health monitoring, convolutional neural networks, machine learning, and other fields, is reviewed. Finally, the future prospects of ultrasonic-guided wave NDT technology are discussed.
... These factors include third-party interference, external corrosion, material failure, and internal corrosion. Each of these factors poses a significant risk to pipeline integrity and underscores the importance of timely detection and identification of defects, particularly localized corrosion [8,9]. ...
... It involves solving a quadratic equation using the coefficients a and b. The y-coordinate of the projection point for the same element pr i y is calculated using Equation (8). The coefficient a is derived from Equation (9), which represents the slope of the line perpendicular to the phased array surface. ...
Pipeline structures are susceptible to corrosion, leading to significant safety, environmental, and economic implications. Existing long range guided wave inspection systems often fail to detect footprints of the concentrated defects, which can lead to leakage. One way to tackle this issue is the utilization of circumferential guided waves that inspect the pipe's cross section. However, achieving the necessary detection resolution typically necessitates the use of high-order modes hindering the inspection data interpretation. This study presents the implementation of an ultrasonic technique capable of detecting and classifying wall thinning and concentrated defects using high-order guided wave modes. The technique is based on a proposed phase velocity mapping approach, which generates a set of isolated wave modes within a specified phase velocity range. By referencing phase velocity maps obtained from defect-free stages of the pipe, it becomes possible to observe changes resulting from the presence of defects and assign those changes to the specific type of damage using artificial neural networks (ANN). The paper outlines the fundamental principles of the proposed phase velocity mapping technique and the ANN models employed for classification tasks that use synthetic data as an input. The presented results are meticulously verified using samples with artificial defects and appropriate numerical models. Through numerical modeling, experimental verification, and analysis using ANN, the proposed method demonstrates promising outcomes in defect detection and classification, providing a more comprehensive assessment of wall thinning and concentrated defects. The model achieved an average prediction accuracy of 92% for localized defects, 99% for defect-free cases, and 98% for uniform defects.
... Meanwhile, model-based agents focus on how the internal model of the environment receives states or determines actions based on those states. Within the environment, various conditions or states have been determined, as well as the actions and consequences that are carried out based on these state conditions [23]. Previous research related to model-free agents, as has been done by Liu et al. [24], implements model-free agents in DRL to control the load frequency. ...
The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in “normal” or “not normal” conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system.
... Motivated by it, this paper presents a holistic review of published articles on WRIA. Unlike previous review articles [26][27][28][29][30] about the subject of WRIA, the 3 main differences between this paper and the previous reviews are summarized as 3Cs: ...
Welding radiographic image analysis (WRIA) is a key technology for welding automated non-destructive testing. Although there already exist some valuable surveys on WRIA, they do not provide a systematic overview of the challenges faced by WRIA and lack a careful distinction and comparison of the core feature techniques in WRIA. With the rapid development of the WRIA area, it is both urgent and challenging to comprehensively review the relevant studies. Therefore, this paper provides an extensive review of 164 papers published in the recent quarter century (1997–2021) ever since its first coined. Three key aspects, namely the challenges faced by WRIA, the evolutionary paths involved in the technology, and the specific application tasks are further discussed in detail. At last, potential future perspectives for WRIA are explored in terms of problem setup, technical improvement, and humanistic care, so as to provide useful insights to both academic researchers and industrial practitioners.
... However, research carried out in real water supply networks shows that, despite the use of these sensors, there is a problem with the lack of leak detection [29][30][31][32]. A frequent solution to this problem is to increase the number of sensors, as well as the additional use of acoustic or vibration sensors [33][34][35]. ...
... The solution to this problem may be the division of the analysed zone into smaller subzones in terms of territory and number of recipients, as recommended by Morrison et al. [20]. It is also worth considering the possibility of equipping such subzones with additional acoustic sensors, which is recommended by Wong and McCann [32]. ...
One of the main tasks that water companies face is limiting water losses through the distribution network. This issue is becoming more and more relevant because of progressive climate changes and rising water resource deficiencies. The first step to reducing water losses is the proper detection of leakages, including their location and size. A common approach, called active leakage control, is to divide the water network into District Metered Areas (DMAs) to detect unreported leaks in the water distribution system (WDS). The operating flow meter device at the border of the DMA allows the determination of the number of water losses by balancing water inflows into the zone and billed water consumption. However, to precisely locate a water leak it is necessary to equip the DMA with an adequate number of pressure sensors. The aim of this paper is to evaluate the impact of water supply DMA formation on the sensitivity of the monitoring system in accordance with the number and location of the pressure sensors and the geometric structure of the water pipes in the DMA in order to successfully detect water leakage. The research was conducted on a model grid network with a constant node number but a differential pipe structure. Subsequently, results were verified in the conditions of a real water supply network. The obtained tests showed no clear relationship between the effectiveness of leak detection and the network complexity but confirmed a significant improvement in leak detection after equipping the monitoring system with an additional pressure gauge.
... Reliable data about WDNs can help their managers make responsible asset management decisions on when and how to make repairs and maintenance. Additionally, leak detection through GPR image analysis has the potential to prevent (i.e. by favoring the detection of water leaks at early stages) the waste of water, energy, and infrastructure [18] in those leaks that can only be identified when damage to a road surface occurs (i.e. when leaked water is visible [19). A raw GPR image may be used to detect and characterize a variety of subsurface assets (e.g. ...
... )[19]. ...
Critical infrastructures such as water distribution networks (WDNs) require reliable and affordable information at a reasonable cost to address challenges that can negatively affect their operation. Inadequate knowledge about WDN assets and their state of health presents challenges for essential activities such as network modeling, operation, assessment, and maintenance. This work seeks to increase the availability of WDN asset data through improved interpretability of GPR images. The semi-automatic labeling approach presented here expands upon existing multi-agent image-cleaning methods and feature characterization techniques. The division of a pre-processed image, in the form of a matrix, into a grid of smaller blocks allowed the identification of relevant features using density of nonzero values in the blocks; this approach, conducted manually in this proof of concept, can provide a basis for training an intelligent system (e.g., a convolutional neural network) to extract the families of interest and eliminate noise. Thus, this research expands this methodology to advance towards automatic detection of pipes and leaks and easily visualize the data. In this paper, 3D visualizations of WDN assets have been created to demonstrate the usefulness of this semi-automatic process in delivering easily-interpretable GPR data for managers and operators of WDNs.
... Ultrasonic guided waves showed good potential detecting various defects in pipeline networks at sufficiently large distances. Conventional guided wave methods use low frequencies where only fundamental modes exist [14,15]. Low frequency guided waves have less attenuation and allow to inspect buried or covered pipe sections at distances up to 100 m. ...
Hidden corrosion defects can lead to dangerous accidents such as leakage of toxic materials causing extreme environmental and economic consequences. Ultrasonic guided waves showed good potential detecting distributed corrosion in pipeline networks at sufficiently large distances. To simplify signal analysis, traditional guided wave methods use low frequencies where only fundamental modes exist; hence, the small, localized defects are usually barely detectable. Novel techniques frequently use higher order guided wave modes that propagate around the circumference of the pipe and are more sensitive to the localized changes in the wall thickness. However current high order mode guided wave technology commonly uses either non-dispersive shear modes or higher order mode cluster (HOMC) waves that are mostly sensitive to surface defects. As the number of application cases of high order modes to corrosion detection is still limited, a huge potential is available to seek for other modes that can offer improved resolution and sensitivity to localized corrosion type defects. The objective of this work was to investigate higher order modes for corrosion detection and to determine the most promising ones in sense of excitability, leakage losses, propagation distance, and potential simplicity in the analysis. The selection of the proper mode is discussed with the support of phase and group velocity dispersion curves, out of plane and in plane distributions over the thickness and on surface of the sample, and leakage losses due to water load. The analysis led to selection of symmetric S3 mode, while the excitation of it was demonstrated through finite element simulations, taking into account the size of phased array aperture and apodization law and considering two-sided mode generation. Finally, theoretical estimations were confirmed with experiments, demonstrating the ability to generate and receive selected mode. It was shown that S3 mode is a good candidate for corrosion screening around the circumference of the pipe, as it has sufficient propagation distance, can be generated with conventional ultrasonic (UT) phased arrays, has sufficiently high group velocity to be distinguished from co-existing modes, and is sensitive to the loss of wall thickness.
