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Guillermo Robles published an article in May 2018.
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Instituto de Ingenieria, Universidad Nacional Autónoma de México, Mexico DF, Mexico
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Article 1 Read 0 Citations Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers Published: 03 May 2018
Sensors, doi: 10.3390/s18051410
Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges in electrical insulation for maintenance based on the condition of the electrical equipment. The use of this phenomenon is a mere example of the capabilities of the proposed method but it is very representative because the emission can be electromagnetic in the VHF and UHF ranges or acoustic. This paper presents a method to locate more than one source in space with only two receivers, one of them in a fixed position and the other describing a circumference around the first one. The signals arriving from the different sources to the antennas are first separated using a classification technique based on their spectral components. Then, the individualized time differences of arrival (TDOA) from the sources collected at different angles describe a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive these functions for any source analytically with the position of the source as unknown parameters. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of each source. Finally, the technique is extended to the localization of the emitter in three dimensions.
Article 1 Read 0 Citations Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of P... Published: 25 February 2018
Energies, doi: 10.3390/en11030486
This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.
Article 0 Reads 2 Citations Survey on the Performance of Source Localization Algorithms Published: 18 November 2017
Sensors, doi: 10.3390/s17112666
The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.
Article 2 Reads 0 Citations Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation Published: 15 November 2017
Sensors, doi: 10.3390/s17112625
The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect.
Conference 1 Read 0 Citations A combined algorithm approach for PD location estimation using RF antennas Published: 01 June 2017
2017 IEEE Electrical Insulation Conference (EIC), doi: 10.1109/eic.2017.8004695
To locate the positions of partial discharge sources in free space at least four RF antennas are arranged in a suitable spatial geometry to detect the radiated electromagnet energy from the discharge. The time-difference-of-arrival (TDOA) between the signals from each antenna are then used within multi-lateration equations to determine the position of the source. The iterative Hyperbolic Least Squares (HLS) method and the non-iterative Maximum Likelihood Estimator (MLE) method are two common techniques used in the literature to solve the multi-lateration equations. This paper investigates the ability of combining MLE and HLS to improve location accuracy and maintain fast location computation time. To this end HLS, MLE and the combined MLE-HLS method are evaluated in terms of location accuracy and computation performance for three spatial antenna configurations, namely Square, Pyramidal and Trapezoidal arrangements. The location accuracies for each method are evaluated for theoretical TDOA values and also for the case when a finite sampling rate of 10G samples-per-second is considered; the latter is implemented through appropriate rounding up of TDOA values by one sample time. It is shown that MLE-HLS produces improved location accuracy compared with HLS and MLE for both theoretical and finite sampled TDOA values. In addition, it is shown that MLE-HLS improves significantly the computation time over the iterative HLS method.
Conference 1 Read 0 Citations A survey of time-of-flight algorithms to determine bone positions in movement Published: 01 May 2017
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), doi: 10.1109/i2mtc.2017.7969710
In biomechanical applications where an ultrasound signal is used to determine the position of a specific organ or tissue, like for example a bone, a so-called A-mode ultrasonography is used. A ultrasonic pulse is generated by a transducer, injected in the tissue to be examined, and then the echoes are received and processed. Echoes are generated by changes in acoustic impedance in the medium, like for example a change of tissue from muscle to bone. To determine the position of the reflecting interface, the time-of-flight is measured and, utilizing well-know values for the transmission speed, the distance or depth is computed. If the localization device is to be designed to be small, wearable, and low-power, it is expected that the signal will be of worse quality with respect to traditional ultrasonography systems, especially under the point of view of signal-to-noise ratio. In these conditions, the reliability of the algorithm that implement the time-of-flight calculation is of paramount importance. In this paper, a simulated soft tissue-bone interface (implemented with an ultrasound gel-pad) has been measured with intentionally low excitation signals and with the presence of imperfections similar to those expected in a physiological system. Several classic algorithms have been tested and benchmarked in this condition, and a new method with better reliability and repeatability is proposed.