Relevant bibliographies by topics / Magnetic anomaly detection sensor

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Magnetic anomaly detection sensor'

Author: Grafiati
Published: 21 April 2022
Last updated: 18 July 2025

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Journal articles on the topic "Magnetic anomaly detection sensor"

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Hu, Bo Zhou, Meng Chun Pan, Peng Jiang, Wu Gang Tian, Jia Fei Hu, and Jing Hua Hu. "The Research on Magnetic Target Detection Technology Based on Wireless Sensor Network." Applied Mechanics and Materials 644-650 (September 2014): 1213–17. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1213.

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In the conditions of magnetic dipole model, this paper proposed forward a centroid localization algorithm on magnetic anomaly target based on wireless sensor network node which distribution are random and the improved the weighted centroid localization algorithm based on magnetic induction intensity. According to the fluctuation of magnetic field intensity which detected by magnetic sensors, that can detect the existence of magnetic anomaly target and its location. Established an experimental system of the wireless sensor network for magnetic anomaly detection whose core designs including the HMC1043 three-axis magnetic resistance sensor and the CC2530 Zigbee RF chip. The experimental results show that the algorithm can accurately positioning the magnetic anomaly target within the network.
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Tianhan, Yang, and Li Shizhong. "The Impact of Magnet Structures on Target Magnetic Anomaly Detection." Journal of Physics: Conference Series 2891, no. 12 (2024): 122009. https://doi.org/10.1088/1742-6596/2891/12/122009.

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Abstract To address the limitation of short detection ranges in conventional magnetic sensors, excitation magnetic fields can be used to enhance the target’s magnetic anomaly and thereby extend the detection range. By arranging permanent magnets to create excitation magnetic fields around the magnetic sensor, targets are magnetized, producing significant magnetic anomalies that can be detected by the sensor. Four different magnet configurations were tested in simulations to compare the response characteristics of the magnetic sensor using a typical iron rod as the target. Based on these simulations, a magnetic detection device was designed and experimentally validated. The results demonstrate that excitation magnetic fields effectively increase the magnetic anomaly and detection range compared to conditions without permanent magnets. When two permanent magnets are placed vertically with the magnetic sensor’s measurement point at the center, the excitation field has the most pronounced effect on the iron rod’s magnetization, resulting in the largest magnetic anomaly and improving signal acquisition. This study offers a novel approach to enhancing the detection capabilities of magnetic sensors.
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Li, Hangcheng, Jiaming Luo, Jiajun Zhang, et al. "Determinants of Maximum Magnetic Anomaly Detection Distance." Sensors 24, no. 12 (2024): 4028. http://dx.doi.org/10.3390/s24124028.

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The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are established, calibrated and analyzed according to the experiment configuration. A maximum detectable distance map as a function of target size and measurement resolution is then obtained from parametric sweeping on an experimentally calibrated finite element analysis model. We find that the logarithm of detectable distance is positively proportional to the logarithm of object size while negatively proportional to the logarithm of resolution, within the ranges of 1 m~500 m and 1 pT~1 μT, respectively. A three-parameter empirical formula (namely distance-size-resolution logarithmic relationship) is firstly developed to determine the most economic sensor configuration for a given detection task, to estimate the maximum detection distance for a given magnetic sensor and object, or to evaluate minimum detectable object size at a given magnetic anomaly detection scenario.
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Liu, Yong, Yu Fang Zhang, and Hong Yi. "The New Magnetic Survey Method for Underwater Pipeline Detection." Applied Mechanics and Materials 239-240 (December 2012): 338–43. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.338.

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Magnetic survey has been applied to detect underwater pipeline location, tracking and depth more and more nowadays. In order to detect such smaller targets, besides the application of high-precision magnetometer detector, an effective method especial for underwater pipeline is needed. This paper is to research the magnetic survey method for detecting the underwater pipeline. In terms of its magnetic anomaly characteristic, magnetic sensor array and magnetic anomaly gradient method which can eliminate geomagnetic diurnal variation are adopted to detect the location, tracking and depth of underwater pipeline. Finally, the experiment has shown that this method has good detection accuracy and value in engineering application.
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Ege, Yavuz, Mustafa Çoramık, Murat Kabadayı, et al. "Anomaly detection with low magnetic flux: A fluxgate sensor network application." Measurement 81 (March 2016): 43–56. http://dx.doi.org/10.1016/j.measurement.2015.12.004.

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Page, Brian R., Reeve Lambert, Nina Mahmoudian, David H. Newby, Elizabeth L. Foley, and Thomas W. Kornack. "Compact Quantum Magnetometer System on an Agile Underwater Glider." Sensors 21, no. 4 (2021): 1092. http://dx.doi.org/10.3390/s21041092.

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This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduction process, test procedure for anomaly mapping, and results from constrained operation featuring underwater motion capture system for ground truth localization. Validation in the noisy and constrained pool environment creates a trajectory towards affordable littoral magnetic anomaly mapping infrastructure. Such a marine sensor technology will be capable of extended operation in challenging areas while providing high-resolution, timely magnetic data to operators for automated detection and classification of marine objects.
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Ge, Jiahao, Jinwu Xiang, and Daochun Li. "Integrated Low Electromagnetic Interference Design Method for Small, Fixed-Wing UAVs for Magnetic Anomaly Detection." Drones 8, no. 8 (2024): 347. http://dx.doi.org/10.3390/drones8080347.

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Unmanned aerial vehicles (UAVs) equipped with magnetic airborne detectors (MADs) represent a new combination for underground or undersea magnetic anomaly detection. The electromagnetic interference (EMI) generated by a UAV platform affects the acquisition of weak magnetic signals by the MADs, which brings unique conceptual design difficulties. This paper proposes a systematic and integrated low-EMI design method for small, fixed-wing UAVs. First, the EMI at the MAD is analyzed. Second, sensor layout optimization for a single UAV is carried out, and the criteria for the sensor layout are given. To enhance UAV stability and resist atmospheric disturbances at sea, the configuration is optimized using an improved genetic algorithm. Then, three typical multi-UAV formations are analyzed. Finally, the trajectory is designed based on an analysis of its influence on EMI at the MAD. The simulation results show that the low-EMI design can keep MADs away from the EMI sources of UAVs and maintain flight stability. The thread-like formation is the best choice in terms of mutual interference and search width. The results also reveal the close relationship between the low-EMI design and flight trajectory. This research can provide a reference for the conceptual design and trajectory optimization of small, fixed-wing UAVs for magnetic anomaly detection.
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Yang, Ruiping, Hongpeng Wang, Huan Liu, Wang Luo, Jian Ge, and Haobin Dong. "A new digital single-axis fluxgate magnetometer according to the cobalt-based amorphous effects." Review of Scientific Instruments 93, no. 3 (2022): 035104. http://dx.doi.org/10.1063/5.0084376.

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Fluxgate sensors are currently widely used for weak magnetic field measurement because of their relatively great performance, such as resolution, power consumption, and measurement of vector magnetic fields directly. Since the analog fluxgate sensor has some drawbacks, e.g., it would be influenced by the noise of the analog circuit. Hence, in recent years, the analog circuit is gradually inclined to be realized by digital processing in which the software parameter adjustment is employed to replace the analog components, which can greatly improve the flexibility of the design. This paper proposes a digital single-axis fluxgate sensor according to the cobalt-based amorphous effect. To be specific, the analog signal output by the fluxgate is sampled directly by an analog-to-digital converter to obtain the signal waveform in digital form after amplification. The demodulation, filtering, and integration of the signal are all solved by mathematical algorithms. Based on the working principle of the fluxgate sensor, the selection of the magnetic core material and coil winding method of the fluxgate sensor probe is introduced in detail. The design and function of the excitation circuit and preamplifier circuit, as well as the specific realization of digital signal processing, are described. Finally, the performance test of the digital fluxgate sensor was performed under laboratory conditions, and the magnetic anomaly detection comparison experiment was performed outdoors with commercial fluxgate sensors. To sum up, the linearity of the digital single-axis fluxgate sensor is better than 1 × 10−5, and the root mean square noise value is below 0.1 nT. At the same time, it has good magnetic field tracking performance and is extremely sensitive to the magnetic field of the measurement area.
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Shen, Ying, Jiazeng Wang, Jiedong Shi, Shuxiang Zhao, and Junqi Gao. "Interpretation of signature waveform characteristics for magnetic anomaly detection using tunneling magnetoresistive sensor." Journal of Magnetism and Magnetic Materials 484 (August 2019): 164–71. http://dx.doi.org/10.1016/j.jmmm.2019.04.016.

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Naghibi, Javad, Kamyar Mehran, and Martin P. Foster. "High-Frequency Non-Invasive Magnetic Field-Based Condition Monitoring of SiC Power MOSFET Modules." Energies 14, no. 20 (2021): 6720. http://dx.doi.org/10.3390/en14206720.

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Current distribution anomaly can be used to indicate the onset of package-related failures modes in Silicon Carbide power MOSFET modules. In this paper, we propose to obtain the wire bond’s magnetic field profile using an array of Tunnel Magneto-Resistance (TMR) sensors, and characterise the small changes in the current density distribution to find the onset of the wire bond degradation processes, including wire bond lift-off, wire bond cracking, and wire bond fracture. We propose a novel condition monitoring technique where a non-galvanic high-bandwidth sensing and a reliability model monitor the health of the power switches. We designed a dedicated calibration set-up to examine the sensor array and calibrated to demonstrate the adequate sensitivity to a minimum 5% current anomaly detection in a single wire bond of the switching devices operating with 50 kHz switching frequency. We use a hardware-in-the-loop (HIL) experimental set-up to replicate wire bond-related failures in a 1200 V/55 A SiC MOSFET power module of a DC/DC Boost converter. Signal conditioning circuits are further designed to amplify and buffer the sensor readings. Experimental results showed the proposed technique is able to detect a wide range of package-related failures.
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Dissertations / Theses on the topic "Magnetic anomaly detection sensor"

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Gameiro, Gonçalo. "Design modifications of a UAV wing for optimal integration of a magnetic anomaly detection sensor." Master's thesis, Academia da Força Aérea; Instituto Superior Técnico, 2018. http://hdl.handle.net/10400.26/40254.

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Supervisors: Prof. Afzal Suleman. Examination Committee: Chairperson: Prof. Filipe Szolnoky Ramos Pinto Cunha; Supervisor: Prof. Afzal Suleman; Member of the Committee: Major Dr. Luís Filipe da Silva Félix<br>This work describes the conceptual design of a Unmanned Air Vehicle (UAV) wing with a Magnetic Anomaly Detection (MAD) sensor for submarine detection operations. Nowadays, underwater marine vessels are able to evade conventional detection methods such as sonar. Therefore, it is necessary to integrate MAD sensors in modern Anti-Submarine Warfare theatres. UAVs typically generate a magnetic field due to the electrical systems on board, causing interference noise on the MAD sensor data analysis and compromising its performance. To address these issues, a characterization of the aircraft’s magnetic signature was conducted, and it was found that the wing tip and a tail stinger boom are the best options to minimize the magnetic noise. A structural and aerodynamic analysis of the aircraft showed the wing tip configuration was the best option since the amount of mass required to counter the moment of a tail stinger boom would require major modifications on the UAV. Also, the aircraft magnetic signature is minimum at the wing tip, with an intensity of -2.9nT. An aerodynamic characterization of the aircraft was carried to evaluate the effect of the MAD pods on the wingtips. A parametric optimization of the wing was conducted. Given the dimensional constraints on the wing structure and a target magnetic noise of 2nT at the wing tip, the optimizer objective function was to minimize the total fuel consumption. The optimum solution allowed a decrease of 30% on the magnetic noise and a fuel consumption of 8.71 kg of fuel for an 8-hour search operation.<br>Este trabalho descreve o processo de projeto conceptual de uma asa de um Veículo Aéreo Não-Tripulado (VANT) com um sensor de anomalias magnéticas (AM) para ser usado em deteção de submarinos. Atualmente, estes veículos estão dotados com capacidades que diminuem as hipóteses de detecão por métodos convencionais, como o sonar. Assim, torna-se necessário integrar sensores de AM em cenários atuais de Guerra Anti-Submarina. Os sistemas aviónicos destas aeronaves geram um campo magnético que causa interferência no sensor de AM, causando ruído nos dados da análise e comprometendo a sua eficiência. Para evitar este problema, realizou-se uma caracterização da assinatura magnética da aeronave, concluindo que as pontas das asas e uma configuração de arpão na cauda seriam as melhores soluções para colocar o sensor, a fim de minimizar a interferência magnética. Estudos estruturais e aerodinâmicos revelaram que a primeira seria a melhor opção, pois a massa necessária para anular o momento gerado na segunda requeria alterações substanciais na estrutura da aeronave. A ponta da asa era também o local com menor nível de assinatura magnética. Realizou-se uma otimização paramétrica da asa da aeronave, considerando os efeitos aerodinâmicos dos invólucros do sensor. Atendendo às restrições no dimensionamento da estrutura da asa e a um valor de interferência magnética, o otimizador teria como objetivo minimizar o consumo total de combustível. A solução ótima permitiu reduzir em 30% o valor da assinatura magnética na ponta da asa e obter uma configuração que, numa missão de patrulha de 8 horas, consome 8.71 kg de combustível.<br>N/A
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Torres, John C. "Geomagnetic Compensation for Low-Cost Crash Avoidance Project." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/492.

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The goal of this work was to compensate for the effects of the Earth’s magnetic field in a vector field magnetic sensor. The magnetic sensor is a part of a low-cost crash avoidance system by Stephane Roussel where the magnetic sensor was used to detect cars passing when it was mounted to a test vehicle. However, the magnetic sensor’s output voltage varied when it changed orientation with respect to the Earth’s magnetic field. This limited the previous work to only analyze detection rates when the test vehicle travelled a single heading. Since one of the goals of this system is to be low-cost, the proposed solution for geomagnetic compensation will only use a single magnetic sensor and a consumer-grade GPS. Other solutions exist for geomagnetic compensation but use extra sensors and can become costly. In order to progress the development of this project into a commercial project, three separate geomagnetic compensation algorithms and a calibration procedure were developed. The calibration procedure compensated for the local magnetic field when the magnetic sensor was mounted to the test vehicle and allowed for consistent magnetic sensor voltage output regardless of the type of test vehicle. The first algorithm, Compensation Scheme 1 (CS1), characterized the local geomagnetic field with a mathematical function from field calibration data. The GPS heading was used as the input and the output is the voltage level of the Earth’s magnetic field. The second algorithm, Compensation Scheme 1.5, used a mathematical model of the Earth’s magnetic field using the International Geomagnetic Reference Field. An algorithm was developed to take GPS coordinates as an input and output the voltage contributed by the mathematical representation of the Earth’s magnetic field. The output voltages from CS1 and CS1.5 were subtracted from the calibrated magnetic sensor data. The third algorithm, Compensation Scheme 2 (CS2), used a high pass filter to compensate for changes of orientation of the magnetic sensor. All three algorithms were successful in compensating for the geomagnetic field and vehicle detection in multiple car headings was possible. Since the goal of the magnetic sensor is to detect vehicles, vehicle detection rates were used to evaluate the effectiveness of the algorithms. The individual algorithms had limitations when used to detect passing cars. Through testing, it was found that CS1 and CS1.5 algorithms were suitable to detect vehicles while stopped in traffic while the CS2 algorithm was suitable vehicle detection while the test vehicle is moving. In order to compensate for the limitations of the individual algorithms, a fused algorithm was developed that used a combination of CS1 and CS2 or CS1.5 and CS2. The vehicle speed was used in order to determine which algorithm to use in order to detect cars. Although the goal of this project is not vehicle detection, the rate of successful vehicle detection was used in order to evaluate the algorithms. The evaluation of the fused algorithm demonstrated the value of using CS1 and CS1.5 to detect vehicles when stopped in traffic, which CS2 algorithm cannot do. For a study conducted in traffic, using the fused algorithm increased vehicle detection rates by 51%-62% from using the CS2 algorithm alone. Since this work successfully compensated for geomagnetic effects of the magnetic sensor, the low-cost crash avoidance system can be further developed since it is no longer limited to driving in a single direction. Other projects that experience unwanted geomagnetic effects in their projects can also implement the knowledge and solutions used in this work.
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Cao, Yichen. "Anomaly Detection on Embedded Sensor Processing Platform." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290893.

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Embedded platforms are often used as a sensor data processing node to collect data and transmit the data to the remote server. Due to the poor performance and power limitation, data processing was often left to the remote server. With the improvement of the computation ability, it is becoming possible to do some partial data processing on the embedded platforms, which would reduce the power and time consumption on the data transmission. Moreover, processing the data locally on the embedded platforms could reduce the dependence on the network. The platform could even do some tasks offline. This project aims to explore effective data analysis methods, especially for anomaly detection, which could be implemented on the embedded platform to be analyzed and detected locally. In this project, we select four methods: Seasonal and Trend Decomposition Using Loess (STL), Autoregressive Integrated Moving Average Model (ARIMA), Vector Autoregression (VAR), Long ShortTerm Memory (LSTM), to implement on the embedded platform ESP32. To test which methods could better fit the platform, we evaluate and compare the result from two aspects: the time overhead and the accuracy. The results show that the STL has the highest detection accuracy, but its time overhead is significantly higher than all other methods. ARIMA has the smallest time overhead and higher accuracy than LSTM and VAR. For LSTM, the method performs better with univariable input than multivariable input. Finally, we discuss the factors that may influence the result and future works.<br>Inbäddade plattformar används ofta som en sensor databehandlingsnod för att samla in och sedan överföra data till fjärrservern. Databehandling lämnades ofta till fjärrservern på grund av den dåliga prestandan och effektbegränsningen. Med förbättrad beräkningsförmåga blir det framkomligt att göra en del databehandling på de inbäddade plattformarna, vilket skulle minska ström och tidsförbrukningen för dataöverföringen. För övrigt kan lokal behandling av data på de inbäddade plattformarna minska beroendet av nätverket. Plattformen kan till och med utföra vissa uppgifter I nedkopplat läge. Detta projekt avser att utforska effektiva dataanalysmetoder särskilt för avvikelsedetektering, som kan verkställas på den inbäddade plattformen för att analyseras och upptäckas lokalt. I det här projektet väljer vi fyra metoder för att införa på den inbäddade plattformen ESP32: Seasonal and Trend Decomposition Using Loess (STL), Autoregressive Integrated Moving Average Model (ARIMA), Vector Autoregression (VAR), Long Short-Term Memory (LSTM). För att testa vilka metoder som bättre passar plattformen utvärderar och jämför vi resultatet med hänsyn till två aspekter: tidsomkostnaderna och noggrannheten. Resultaten visar att STL har den högsta detektionsnoggrannheten, men dess tidsomkostning är betydligt högre än alla andra metoder. ARIMA har den minsta tidsomkostningen och högre noggrannhet än LSTM och VAR. För LSTM fungerar metoden bättre med univariable input än multivariable input. Slutligen diskuterar vi faktorerna som möjligtvis påverkar resultatet och framtida arbeten.
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Conde, Erick F. "Environmental Sensor Anomaly Detection Using Learning Machines." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/1050.

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The problem of quality assurance/quality control (QA/QC) for real-time measurements of environmental and water quality variables has been a field explored by many in recent years. The use of in situ sensors has become a common practice for acquiring real-time measurements that provide the basis for important natural resources management decisions. However, these sensors are susceptible to failure due to such things as human factors, lack of necessary maintenance, flaws on the transmission line or any part of the sensor, and unexpected changes in the sensors' surrounding conditions. Two types of machine learning techniques were used in this study to assess the detection of anomalous data points on turbidity readings from the Paradise site on the Little Bear River, in northern Utah: Artificial Neural Networks (ANNs) and Relevance Vector Machines (RVMs). ANN and RVM techniques were used to develop regression models capable of predicting upcoming Paradise site turbidity measurements and estimating confidence intervals associated with those predictions, to be later used to determine if a real measurement is an anomaly. Three cases were identified as important to evaluate as possible inputs for the regression models created: (1) only the reported values from the sensor from previous time steps, (2) reported values from the sensor from previous time steps and values of other water types of sensors from the same site as the target sensor, and (3) adding as inputs the previous readings from sensors from upstream sites. The decision of which of the models performed the best was made based on each model's ability to detect anomalous data points that were identified in a QA/QC analysis that was manually performed by a human technician. False positive and false negative rates for a range of confidence intervals were used as the measure of performance of the models. The RVM models were able to detect more anomalous points within narrower confidence intervals than the ANN models. At the same time, it was shown that incorporating as inputs measurements from other sensors at the same site as well as measurements from upstream sites can improve the performance of the models.
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Garcia, Font Víctor. "Anomaly detection in smart city wireless sensor networks." Doctoral thesis, Universitat Oberta de Catalunya, 2017. http://hdl.handle.net/10803/565607.

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Aquesta tesi proposa una plataforma de detecció d’intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s’identifiquen els diversos passos necessaris des de la recollida de dades fins a l’execució de les tècniques de detecció d’intrusions i s’avalua que el procés sigui escalable i capaç de gestionar dades típiques de ciutats intel·ligents. A més, es comparen diversos algorismes de detecció d’anomalies i s’observa que els mètodes de vectors de suport d’una mateixa classe (one-class support vector machines) resulten la tècnica multivariant més adequada per a descobrir atacs tenint en compte les necessitats d’aquest context. Finalment, es proposa un esquema per a ajudar els administradors a identificar els tipus d’atacs rebuts a partir de les alarmes disparades.<br>Esta tesis propone una plataforma de detección de intrusiones para revelar ataques en las redes de sensores inalámbricas (WSN, por las siglas en inglés) de las ciudades inteligentes (smart cities). La plataforma está diseñada teniendo en cuenta la necesidad de los administradores de la ciudad inteligente, los cuales necesitan acceso a una arquitectura centralizada que pueda gestionar alarmas de seguridad en un sistema altamente heterogéneo y distribuido. En esta tesis se identifican los varios pasos necesarios desde la recolección de datos hasta la ejecución de las técnicas de detección de intrusiones y se evalúa que el proceso sea escalable y capaz de gestionar datos típicos de ciudades inteligentes. Además, se comparan varios algoritmos de detección de anomalías y se observa que las máquinas de vectores de soporte de una misma clase (one-class support vector machines) resultan la técnica multivariante más adecuada para descubrir ataques teniendo en cuenta las necesidades de este contexto. Finalmente, se propone un esquema para ayudar a los administradores a identificar los tipos de ataques recibidos a partir de las alarmas disparadas.<br>This thesis proposes an intrusion detection platform which reveals attacks in smart city wireless sensor networks (WSN). The platform is designed taking into account the needs of smart city administrators, who need access to a centralized architecture that can manage security alarms in a highly heterogeneous and distributed system. In this thesis, we identify the various necessary steps from gathering WSN data to running the detection techniques and we evaluate whether the procedure is scalable and capable of handling typical smart city data. Moreover, we compare several anomaly detection algorithms and we observe that one-class support vector machines constitute the most suitable multivariate technique to reveal attacks, taking into account the requirements in this context. Finally, we propose a classification schema to assist administrators in identifying the types of attacks compromising their networks.
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Menglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.

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Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method
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Vignisson, Egill. "Anomaly Detection in Streaming Data from a Sensor Network." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257507.

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In this thesis, the use of unsupervised and semi-supervised machine learning techniques was analyzed as potential tools for anomaly detection in the sensor network that the electrical system in a Scania truck is comprised of. The experimentation was designed to analyse the need for both point and contextual anomaly detection in this setting. For the point anomaly detection the method of Isolation Forest was experimented with and for contextual anomaly detection two different recurrent neural network architectures using Long Short Term Memory units was relied on. One model was simply a many to one regression model trained to predict a certain signal, while the other was an encoder-decoder network trained to reconstruct a sequence. Both models were trained in an semi-supervised manner, i.e. on data that only depicts normal behaviour, which theoretically should lead to a performance drop on abnormal sequences resulting in higher error terms. In both setting the parameters of a Gaussian distribution were estimated using these error terms which allowed for a convenient way of defining a threshold which would decide if the observation would be flagged as anomalous or not. Additional experimentation's using an exponential weighted moving average over a number of past observations to filter the signal was also conducted. The models performance on this particular task was very different but the regression model showed a lot of promise especially when combined with a filtering preprocessing step to reduce the noise in the data. However the model selection will always be governed by the nature the particular task at hand so the other methods might perform better in other settings.<br>I den här avhandlingen var användningen av oövervakad och halv-övervakad maskininlärning analyserad som ett möjligt verktyg för att upptäcka avvikelser av anomali i det sensornätverk som elektriska systemet en Scanialastbil består av. Experimentet var konstruerat för att analysera behovet av både punkt och kontextuella avvikelser av anomali i denna miljö. För punktavvikelse av anomali var metoden Isolation Forest experimenterad med och för kontextuella avvikelser av anomali användes två arkitekturer av återkommande neurala nätverk. En av modellerna var helt enkelt många-till-en regressionmodell tränad för att förutspå ett visst märke, medan den andre var ett kodare-avkodare nätverk tränat för att rekonstruera en sekvens.Båda modellerna blev tränade på ett halv-övervakat sätt, d.v.s. på data som endast visar normalt beteende, som teoretiskt skulle leda till minskad prestanda på onormala sekvenser som ger ökat antal feltermer. I båda fallen blev parametrarna av en Gaussisk distribution estimerade på grund av dessa feltermer som tillåter ett bekvämt sätt att definera en tröskel som skulle bestämma om iakttagelsen skulle bli flaggad som en anomali eller inte. Ytterligare experiment var genomförda med exponentiellt viktad glidande medelvärde över ett visst antal av tidigare iakttagelser för att filtera märket. Modellernas prestanda på denna uppgift var välidt olika men regressionmodellen lovade mycket, särskilt kombinerad med ett filterat förbehandlingssteg för att minska bruset it datan. Ändå kommer modelldelen alltid styras av uppgiftens natur så att andra metoder skulle kunna ge bättre prestanda i andra miljöer.
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Abuaitah, Giovani Rimon. "ANOMALIES IN SENSOR NETWORK DEPLOYMENTS: ANALYSIS, MODELING, AND DETECTION." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1376594068.

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Dudek, Denise Miriam [Verfasser]. "Lightweight Anomaly Detection for Wireless Sensor Networks / Denise Miriam Dudek." München : Verlag Dr. Hut, 2015. http://d-nb.info/1075409012/34.

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Zhao, Jingjun. "A Two-phase Security Mechanism for Anomaly Detection in Wireless Sensor Networks." Diss., North Dakota State University, 2013. https://hdl.handle.net/10365/26498.

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Wireless Sensor Networks (WSNs) have been applied to a wide range of application areas, including battle fields, transportation systems, and hospitals. The security issues in WSNs are still hot research topics. The constrained capabilities of sensors and the environments in which sensors are deployed, such as hostile and non-reachable areas, make the security more complicated. This dissertation describes the development and testing of a novel two-phase security mechanism for hierarchical WSNs that is capable of defending both outside and inside attacks. For the outside attacks, the attackers are usually malicious intruders that entered the network. The computation and communication capabilities of the sensors restrict them from directly defending the harmful intruders by performing traditionally encryption, authentication, or other cryptographic operations. However, the sensors can assist the more powerful nodes in a hierarchical structured WSN to track down these intruders and thereby prevent further damage. To fundamentally improve the security of a WSN, a multi-target tracking algorithm is developed to track the intruders. For the inside attacks, the attackers are compromised insiders. The intruders manipulate these insiders to indirectly attack other sensors. Therefore, detecting these malicious insiders in a timely manner is important to improve the security of a network. In this dissertation, we mainly focus on detecting the malicious insiders that try to break the normal communication among sensors, which creates holes in the WSN. As the malicious insiders attempt to break the communication by actively using HELLO flooding attack, we apply an immune-inspired algorithm called Dendritic Cell Algorithm (DCA) to detect this type of attack. If the malicious insiders adopt a subtle way to break the communication by dropping received packets, we implement another proposed technique, a short-and-safe routing (SSR) protocol to prevent this type of attack. The designed security mechanism can be applied to different sizes of both static and dynamic WSNs. We adopt a popular simulation tool, ns-2, and a numerical computing environment, MATLAB, to analyze and compare the computational complexities of the proposed security mechanism. Simulation results demonstrate effective performance of the developed corrective and preventive security mechanisms on detecting malicious nodes and tracking the intruders.
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Books on the topic "Magnetic anomaly detection sensor"

1

Baus, Wolfgang W. Magnetic anomaly detection using conventional and superconductive sensors with respect to vehicle monitoring. Brockmeyer, 1995.

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Forrest, R. N. A program to compute magnetic anomaly detection probabilities. Naval Postgraduate School, 1988.

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Forrest, R. N. A program to compute magnetic anomaly detection probabilities. 2nd ed. Naval Postgraduate School, 1990.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7467-7.

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Liotta, Antonio, Hedde Bosman, and Giovanni Iacca. Smart Anomaly Detection for Sensor Systems: Computational Intelligence Techniques for Sensor Networks and Applications. Springer London, Limited, 2020.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer, 2019.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer, 2018.

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Gearhart, Robert. Archaeological Interpretation of Marine Magnetic Data. Edited by Ben Ford, Donny L. Hamilton, and Alexis Catsambis. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199336005.013.0004.

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Interpreting remote sensing data is one of the most important tasks of archaeologists working in submerged environments. Researchers rely on remote-sensing technologies to aid their search for historic shipwrecks of interest. Magnetometers are essential for detection of buried shipwrecks. The main goal of magnetic interpretation has been to distinguish shipwrecks from debris, usually resulting in an archaeological assessment of each anomaly concerning its potential for historic significance. The past two decades have seen improvement in archaeologists' abilities to detect shipwreck anomalies. This article provides a basic, nonmathematical summary of magnetism relevant to archaeological interpretation and the evolving perceptions of shipwreck anomalies. The basis for assessing magnetic anomaly significance must be firmly rooted in empiricism in order to improve the objectivity of data interpretation.
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Book chapters on the topic "Magnetic anomaly detection sensor"

1

Xu, W., Z. Y. Guo, P. Wu, and Z. X. Liu. "Detection Optimization of Single-Axis Magnetic Anomaly Sensor for Pig Clog Locating." In Proceedings of the International Field Exploration and Development Conference 2018. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7127-1_177.

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Shukla, Mayank, Sneha Yadav, Abhay Pratap Singh, Fizza Rizvi, and Surya Vikram Singh. "Anomaly detection in wireless sensor network." In Emerging Trends in Computer Science and Its Application. CRC Press, 2025. https://doi.org/10.1201/9781003606635-100.

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Farruggia, Alfonso, Giuseppe Lo Re, and Marco Ortolani. "Probabilistic Anomaly Detection for Wireless Sensor Networks." In AI*IA 2011: Artificial Intelligence Around Man and Beyond. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23954-0_44.

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Fang, Yi, Olufemi A. Omitaomu, and Auroop R. Ganguly. "Incremental Anomaly Detection Approach for Characterizing Unusual Profiles." In Knowledge Discovery from Sensor Data. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12519-5_11.

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Chang, Marcus, Andreas Terzis, and Philippe Bonnet. "Mote-Based Online Anomaly Detection Using Echo State Networks." In Distributed Computing in Sensor Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02085-8_6.

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Salem, Osman, Alexey Guerassimov, Ahmed Mehaoua, Anthony Marcus, and Borko Furht. "Anomaly Detection Scheme for Medical Wireless Sensor Networks." In Handbook of Medical and Healthcare Technologies. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8495-0_8.

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Li, Han, Bin Yu, and Ting Zhao. "An Anomaly Pattern Detection Method for Sensor Data." In Web Information Systems and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_28.

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Zhu, Qianwen, Jinyu Zhou, Shiyu Zhao, and Wei Wang. "Graph-Based Anomaly Detection of Wireless Sensor Network." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9423-3_18.

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Rabatel, Julien, Sandra Bringay, and Pascal Poncelet. "SO_MAD: SensOr Mining for Anomaly Detection in Railway Data." In Advances in Data Mining. Applications and Theoretical Aspects. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03067-3_16.

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Chatterjee, Aditi, and Kiranmoy Das. "State Estimation and Anomaly Detection in Wireless Sensor Networks." In Emerging Wireless Communication and Network Technologies. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0396-8_16.

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Conference papers on the topic "Magnetic anomaly detection sensor"

1

Hu, Xinyue. "Influence of Attitude Changes on Magnetic Measurement Accuracy in UAV Magnetic Anomaly Detection." In 2025 5th International Conference on Sensors and Information Technology (ICSI). IEEE, 2025. https://doi.org/10.1109/icsi64877.2025.11009300.

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Roignant, Timothée, Nicolas Le Josse, Abdel Boudraa, Jean-Jacques Szkolnik, Paul Penven, and Hugues Henocq. "Magnetic Anomaly Detection Using Noise-Optimized Orthonormalized Functions on Dual Magnetometric Sensor Signals." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715193.

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Yuan, Zi-Fan, Xin-Gen Liu, and Ming-Yao Xia. "CBAM-Based Residual Network for Magnetic Anomaly Target Detection." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641654.

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Das, Kaushik, Sankhapani Neog, Priyanuj Bora, Digbijoy Chettry, and Rishab Bora. "Anomaly Detection in Sensor Data using Machine Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724176.

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Sheng, Xun, Min Hu, Gang Yu, Li Teng, and Donghua Su. "Anomaly Detection of Sensor Data Based on Similarity." In 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869813.

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Chenevas-Paule, C., S. Zozor, L. L. Rouve, O. J. J. Michel, O. Pinaud, and R. Kukla. "On an Analytical Orthonormal Multipolar Basis for Magnetic Anomaly Detection." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715174.

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Lv, Yumeng, Congyu Liao, and Huan Liu. "Frequency Domain-Based Low-Rank Approximation for Magnetic Anomaly Detection." In 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2024. https://doi.org/10.1109/icsmd64214.2024.10920537.

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Xiao, Tianshun, and Feng Zeng. "AnomalyTK: A Multivariate Time Series Anomaly Detection Method." In 2025 International Conference on Sensor-Cloud and Edge Computing System (SCECS). IEEE, 2025. https://doi.org/10.1109/scecs65243.2025.11065321.

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Neto, Almir, Luis Gomes, and Zita Vale. "Virtual Sensor for Door Opening Detection and Anomaly Detection Using Machine Learning." In 2024 22nd International Conference on Intelligent Systems Applications to Power Systems (ISAP). IEEE, 2024. http://dx.doi.org/10.1109/isap63260.2024.10744280.

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Kaliappan, Vishnu Kumar, Calvin Abel Mathews, Kevin Samuel C, and Dharani Jaganathan. "Temporal-Spatial based Deep Anomaly Detection (TS-DAD) Model : A Steering Sensor Anomaly Detection in Unmanned Ground Vehicles." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11035346.

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Reports on the topic "Magnetic anomaly detection sensor"

1

Forrest, R. N. A Program to Compute Magnetic Anomaly Detection Probabilities. Revision 2. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada225427.

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Sadler, Laurel C., Robert Winkler, and Niranjan Suri. Anomaly Detection for Data Reduction in an Unattended Ground Sensor (UGS) Field. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609445.

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Smith, Carl H., and Robert W. Schneider. Very Dense Magnetic Sensor Arrays for Precision Measurement and Detection. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada453763.

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Griffin, Stephen M., and L. L. Helms. Sub-Audio Magnetics: Miniature Sensor Technology for Simultaneous Magnetic and Electromagnetic Detection of UXO. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada578948.

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Nestleroth. L52298 Augmenting MFL Tools With Sensors that Assess Coating Condition. Pipeline Research Council International, Inc. (PRCI), 2009. http://dx.doi.org/10.55274/r0010396.

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External coatings are routinely used to protect transmission pipelines from corrosion; however, coatings may degrade or disbond over time enabling corrosion to occur. Transmission pipeline operators often use magnetic flux leakage (MFL) in-line inspection tools to detect metal loss corrosion defects. Rather than finding the cause of a problem, failure of the coating within a corrosive environment, MFL corrosion surveys only find the result of the problem, corrosion defects that may permanently alter the pressure carrying capacity of the pipeline. Stress corrosion cracking (SCC) can be detected using in-line inspection (ILI) technology, but the availability of tools is limited and the cost of inspection is high compared to MFL inspection. SCC almost always occurs at coating faults; direct coating assessment could indicate future problems that could degrade the serviceability of the pipeline. In this project, a new sensor was developed to assess external coating that could work with currently available ILI tools for minimal additional cost to perform the inspection. The sensors, electromagnetic acoustic transducers (EMATs), generate ultrasonic waves that are guided by the pipe material around the circumference of the pipe. The coating material and adherence can influence the propagation of the ultrasonic waves; changes in ultrasonic signal features were attributed to coating faults. This development used modeling and experiments to establish a more optimal configuration for coating assessment. A multiple feature approach was used. A commonly used feature, signal amplitude, provided good sensitivity to coating condition but was influenced by inspection variables. One unique feature identified in this development is arrival time of the ultrasonic wave. For the wave type and frequency selected, the wave velocity was different for bare and coated pipe. Therefore, disbonded or missing coating can be detected by monitoring arrival time of the ultrasonic wave, a feature that is amplitude independent. Another feature for assessing coating, absorption of selective frequencies, was also demonstrated. Coating assessment capability was experimentally demonstrated using a prototype EMAT ILI tool. All three detection features were shown to perform well in an ILI environment as demonstrated at Battelle"s Pipeline Simulation Facility and BJ Inspection Services pull rigs. Improvement to the prototype occurred between each test; the most significant improvement was the design and construction of a novel set of thick-trace transmitting and receiving Printed Circuit Board (PCB) EMAT coils. Implementation variables such as moisture and soil loading were shown to have a minimal influence on results.
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