Location identification using a magnetic-field-based FFT signature

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated to the modern mobile devices. These sensory devices allow us to exploit not only infrastructures made for every day use, such as Wifi, but also natural infrastructure, as is the case of natural magnetic fields. In this work, we propose a novel approach that takes advantage of the benefits of using the magnetic sensor incorporated in most modern mobile devices, and the negligible variations of the Earth's magnetic field to position an individual with high accuracy. Most importantly, the methodology proposed allows us to avoid the burden of having to collect magnetic information in different directions in order to construct an accurate magnetic map, showing an improvement on methods that require the individuals to construct bigger magnetic maps that contain redundant information such as magnitude in different directions

A New Approach for Broken Rotor Bar Detection in Induction Motors Using Frequency Extraction in Stray Flux Signals

This paper offers a reliable solution to the detection of broken rotor bars in induction machines with a novel methodology, which is based on the fact that the fault-related harmonics will have oscillating amplitudes due to the speed ripple effect. The method consists of two main steps: Initially, a time-frequency transformation is used and the focus is given on the steady-state regime; thereupon, the fault-related frequencies are handled as periodical signals over time and the classical fast Fourier transform is used for the evaluation of their own spectral content. This leads to the discrimination of subcomponents related to the fault and to the evaluation of their amplitudes. The versatility of the proposed method relies on the fact that it reveals the aforementioned signatures to detect the fault, regardless of the spatial location of the broken rotor bars. Extensive finite element simulations on a 1.1 MW induction motor and experimental testing on a 1.1 kW induction motor lead to the conclusion that the method can be generalized on any type of induction motor independently from the size, power, number of poles, and rotor slot numbers

Detection of inter-turn faults in multi-phase ferrite-PM assisted synchronous reluctance machine

Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currentsPeer ReviewedPostprint (published version

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set

OSEM : occupant-specific energy monitoring.

Electricity has become prevalent in modern day lives. Almost all the comforts people enjoy today, like home heating and cooling, indoor and outdoor lighting, computers, home and office appliances, depend on electricity. Moreover, the demand for electricity is increasing across the globe. The increasing demand for electricity and the increased awareness about carbon footprints have raised interest in the implementation of energy efficiency measures. A feasible remedy to conserve energy is to provide energy consumption feedback. This approach has suggested the possibility of considerable reduction in the energy consumption, which is in the range of 3.8% to 12%. Currently, research is on-going to monitor energy consumption of individual appliances. However, various approaches studied so far are limited to group-level feedback. The limitation of this approach is that the occupant of a house/building is unaware of his/her energy consumption pattern and has no information regarding how his/her energy-related behavior is affecting the overall energy consumption of a house/building. Energy consumption of a house/building largely depends on the energy-related behavior of individual occupants. Therefore, research in the area of individualized energy-usage feedback is essential. The OSEM (Occupant-Specific Energy Monitoring) system presented in this work is capable of monitoring individualized energy usage. OSEM system uses the electromagnetic field (EMF) radiated by appliances as a signature for appliance identification. An EMF sensor was designed and fabricated to collect the EMF radiated by appliances. OSEM uses proximity sensing to confirm the energy-related activity. Once confirmed, this activity is attributed to the occupant who initiated it. Bluetooth Low Energy technology was used for proximity sensing. This OSEM system would provide a detailed energy consumption report of individual occupants, which would help the occupants understand their energy consumption patterns and in turn encourage them to undertake energy conservation measures

Integration of magnetic residuals,derivates and located euler deconvolution for structural and geologic mapping of parts of the precambrian gneisses of Ago-Iwoye, Southwestern Nigeria

Ground based magnetic survey conducted between longitude 06O 55I 51IIN –06O 55I 54IIN and latitude 03O 52I 06IIE –03O 52I 4.8IIE (Olabisi Onabanjo University) remarkably revealed a consistent subsurface NW -SE structural azimuth of localized discontinuities within the shallowly buried heterogeneous basement rocks, which at exposed locations are composed of strongly foliated granite gneiss and migmatite-gneiss with veins and veinlets principally orientated in NNW –SSE direction.Magnetic survey of the area was preceded by site inspection to avoid metallic objects interferences. Field procedure in the area involved Cartesian gridding, base station establishment, data acquisition at gridded points, and repeated bihourly diurnal checksat the base station. At the processing stage, diurnal variation effect was aptly removed before subjection to Kriging (gridding). The gridded data was then prepared as input for Forward Fourier Filter Transform (FFT), which upon definition and implementation enabled Butterworth filtering of isolated ringing effects, reduction to the equator (RTE) for geomagnetic correction, and the use of Gaussian and Upward Continuation filtering for regional magnetic intensity trend determination. Removal of the regional magnetic intensity (RMI) from the total magnetic intensity (TMI) resulted in the derivation of the residual anomaly. Enhancement filters adopted for better resolution of the residual magnetic gradient include analytical signal (AS), tilt-angle derivative (TDR), vertical derivative deconvolution (VDD), and the first vertical derivatives (FVD).TMI and RMI values range between 32925nT –33050nT and 32935nT –333050nT respectively, while the residual gradient ranges between 15nT/m and10nT/m; AS ranges between 0.28nT/m and4.1nT/m; and TDR ranges from-1.4nT/m to 1.4nT/m. Source depth calculation estimated from power spectrum analysis and Euler deconvolution ranges between 1m and15m. Composite overlay of magnetic maps revealed jointed and faulted zones within the area; exhibiting a NW-SE principal azimuth of Liberian orogenic impress, which are in consistence with the foliation direction of the jagged foliated bedrock with an estimated maximum overburden of about 15m.The structural significance of this area as a prospective hydro-geological centre, and as an undesirable spot for high-rise building has been accurately evaluated from research findings. Application of integrated geophysical approach, complemented by detailed geological studies may furnish greater information about the subsurface structural architecture.Keywords:Gneisses; Ground Magnetic Surveying;RTE;Structural discontinuities;TDR.1INTRODUCTIONStructuralmapping is an integral part of geologic surveys. It involves measurements, analyses, interpretation and recognition of geometrical features (structures) generated by rock deformations [1]. These structures often serve as fountains of environmental challenges or unparalleled opportunities depending on their modesof occurrences, which in most cases are imminently controlled by the dynamic interplay of differential stress distributions within the earth interior. In line with the principle of uniformitarianism, a broad understanding about Earth’s paleo processes and internal workingsare deductible from the various deformation types for diverse applications. Deductible inferences from brittle deformationsinclude the kinematics of crustal blocks, orientation of principal axes of regional and local stresses, and geometry. Deeper insights indeep seated stresses, regional movements and block motions are obtainable from ductile deformations

An electromagnetic imaging system for metallic object detection and classification

PhD ThesisElectromagnetic imaging currently plays a vital role in various disciplines, from engineering to medical applications and is based upon the characteristics of electromagnetic fields and their interaction with the properties of materials. The detection and characterisation of metallic objects which pose a threat to safety is of great interest in relation to public and homeland security worldwide. Inspections are conducted under the prerequisite that is divested of all metallic objects. These inspection conditions are problematic in terms of the disruption of the movement of people and produce a soft target for terrorist attack. Thus, there is a need for a new generation of detection systems and information technologies which can provide an enhanced characterisation and discrimination capabilities. This thesis proposes an automatic metallic object detection and classification system. Two related topics have been addressed: to design and implement a new metallic object detection system; and to develop an appropriate signal processing algorithm to classify the targeted signatures. The new detection system uses an array of sensors in conjunction with pulsed excitation. The contributions of this research can be summarised as follows: (1) investigating the possibility of using magneto-resistance sensors for metallic object detection; (2) evaluating the proposed system by generating a database consisting of 12 real handguns with more than 20 objects used in daily life; (3) extracted features from the system outcomes using four feature categories referring to the objects’ shape, material composition, time-frequency signal analysis and transient pulse response; and (4) applying two classification methods to classify the objects into threats and non-threats, giving a successful classification rate of more than 92% using the feature combination and classification framework of the new system. The study concludes that novel magnetic field imaging system and their signal outputs can be used to detect, identify and classify metallic objects. In comparison with conventional induction-based walk-through metal detectors, the magneto-resistance sensor array-based system shows great potential for object identification and discrimination. This novel system design and signal processing achievement may be able to produce significant improvements in automatic threat object detection and classification applications.Iraqi Cultural Attaché, Londo