Hilton de Oliveira Mota

A real-time processing system for denoising of partial discharge signals using the wavelet transform

The wavelet transform (WT) has been proved to be an efficient tool for partial discharge (PD) processing due its capability to stand out inhomogeneous and localized signal features. In previous work, some authors have investigated issues related to the ways to choose a mother wavelet, strategies to denoise PDs and its in field usability. This paper presents a realtime system for partial discharge signal denoising and processing using the discrete wavelet transform (DWT). Real-time processing is an important feature for PD analysis since it allows data acquisition for long periods, leading to a better statistical characterization. Moreover, real-time processing presents benefits for the investigation of new diagnostic procedures and the development of more specific PD applications, like on-line monitoring. The developed system can be used as a preprocessing unit coupled to an existing PD analyzer or a standalone unit employed both for PD acquisition and denoising. Continuous stream processing is carried out by an appropriate border treatment both during DWT processing and PD extraction. Deterministic behavior is guaranteed by a dedicated hardware architecture associated to a real-time operating system. Hardware based optimizations allowed acquisition rates comparable to commercial PD analyzers. The results were obtained with a focus on performance evaluation in terms of computational loads, storage requirements and noise removal for several wavelet filters, decomposition levels and denoising techniques.

Temporal contexts: Effective text classification in evolving document collections

The management of a huge and growing amount of information available nowadays makes Automatic Document Classification (ADC), besides crucial, a very challenging task. Furthermore, the dynamics inherent to classification problems, mainly on the Web, make this task even more challenging. Despite this fact, the actual impact of such temporal evolution on ADC is still poorly understood in the literature. In this context, this work concerns to evaluate, characterize and exploit the temporal evolution to improve ADC techniques. As first contribution we highlight the proposal of a pragmatical methodology for evaluating the temporal evolution in ADC domains. Through this methodology, we can identify measurable factors associated to ADC models degradation over time. Going a step further, based on such analyzes, we propose effective and efficient strategies to make current techniques more robust to natural shifts over time. We present a strategy, named temporal context selection, for selecting portions of the training set that minimize those factors. Our second contribution consists of proposing a general algorithm, called Chronos, for determining such contexts. By instantiating Chronos, we are able to reduce uncertainty and improve the overall classification accuracy. Empirical evaluations of heuristic instantiations of the algorithm, named WindowsChronos and FilterChronos, on two real document collections demonstrate the usefulness of our proposal. Comparing them against state-of-the-art ADC algorithms shows that selecting temporal contexts allows improvements on the classification accuracy up to 10%. Finally, we highlight the applicability and the generality of our proposal in practice, pointing out this study as a promising research direction.

A comparison of cycle spinning versus stationary wavelet transform for the extraction of features of partial discharge signals

This paper presents a comparison of three feature extraction methods to denoise partial discharge (PD) signals. The denoising technique employs the Stationary Wavelet Transform (SWT) associated to a spatially-adaptive selection procedure based on the coefficients propagation along decomposition levels (scales). The PD and noise related coefficients are identified and separated by an automatic data classifier using Support Vector Machines (SVM). The first and second feature extraction methods act directly on the SWT coefficients and differ only on the procedures to characterize the propagation. The third method relies on Cycle Spinning (CS) on the several translated Discrete Wavelet Transform (DWT) obtained from SWT. We conducted an empirical study using Analysis of Variance (ANOVA) to evaluate the influence of the methods on denoising performance and to guarantee the statistical significance of the tests. Afterwards, performance was evaluated considering real PD signals measured in air and in solid dielectrics, corrupted by several types of interferences, both stationary and time-varying. The results show that the three approaches allow robust signal recovering and significant noise rejection, but differ substantially on the quality of the reconstructed signals.

Data processing system for denoising of signals in real-time using the wavelet transform

This paper describes a system able to acquire, process and eliminate noise in continuous streams of data in real-time. The signal processing algorithms were based on the discrete wavelet transform and employ a new approach to deal with border problems, allowing to process the data continuously. The system was implemented using a DSP coupled to a digitizer through its external memory bus to guarantee deterministic behavior while maintaining some degree of flexibility in its configuration. The achieved performance and potential applications are discussed at the end of the text.

Mining undecimated Wavelet Transform maxima lines: An effective way to denoise partial discharge signals

On-site and on-line partial discharge (PD) measurements are well recognized as difficult tasks due to the large scale and diversity of interferences usually encountered in high voltage facilities. Several techniques have been proposed in literature to improve test conditions on the field, based either on analog and digital approaches. More recently, wavelets and their derivatives have shown to be very effective for the processing of PD signals due to their potential to identify transient, time-localized signals. This paper presents the development and results of a new technique to denoise PD signals based on data mining concepts applied to Undecimated Wavelet Transform (UWT) modulus maxima lines propagation. The theory of modulus maxima lines is introduced and their use is justified as an effective way to identify and localize PD pulses. The UWT was employed as a way to increase robustness against the effects of decimation, which is a characteristic of the orthogonal Wavelet Transform that causes random losses of PD pulses. We took advantage of the improved UWT capability to recover signals by using a modified version of the cycle spinning approach. Denoising was performed by separating the maxima lines related to PDs from those related to noise, followed by reconstruction using the inverse UWT. Separation was achieved by the use of data mining tools. The performances of several algorithms were evaluated and compared, including investigations regarding statistically divergent noise types, effects of data pre-processing like normalization and cleansing, procedures for training and comparisons of supervised and unsupervised techniques. Results obtained for both simulated and measured PD signals confirm that the approach is superior when compared to standard linear filters and non-linear wavelet-based techniques. This is particularly noticeable when processing non-stationary time-localized interferences, a situation in which these techniques fail completely.

A partial discharge data acquisition system based on programmable digital oscilloscopes

This paper describes a partial discharge measurement system based on a programmable digital oscilloscope. The system enables the acquisition of partial discharge data both on phase-resolved and time-resolved basis, thus allowing these two analyses. After the acquisition of partial discharge data, statistical operators are applied over the phase distributions, as a way to identify the type of defect present on the equipment under test and its corresponding degradation level. To check for the system performance and develop an initial database on partial discharges, it was used to measure partial discharges in air and solid insulation, and their main characteristics were determined. These measurements were carried out in a measurement cell developed with great care to prevent signal deterioration, allowing the registration of almost distortion-free PD pulses.

A wireless sensor network for the biomechanical analysis of the gait

In this paper a data acquisition system based on wireless sensor networks to support the biomechanical gait analysis will be presented. The system is composed by sensor nodes that can be fasten to the limbs of a person. Each node contains the ESP8266 platform connected to a MPU6050 module that includes a gyroscope and an accelerometer. The system starts working in a self-adjusting (auto calibration) phase. Data-acquisition follows next when the signal from the accelerometer is converted in degrees and the one from the gyroscope in degrees per second. In the host computer data from the gyro are modeled employing a rotational matrix applying the Euler angle method and the quaternions. The outputs of both sensors are combined by means of a complementary filter and the information so obtained confirms that the body is in motion. A special mechanical device was designed to validate sensor measurements. New improvements will be soon incorporated, even though the results obtained so far testify that it already can be used as an inexpensive substitute for the costly opto-tracking cameras.

Partial discharge signal processing using overcomplete dictionaries and sparse representations

Techniques for partial discharge (PD) processing have been increasing in recent researches, since on-site PD measurement can provide valuable information about electrical equipments and their insulation. A lot of approaches for PD filtering have been presented in the literature, mainly based on classical techniques of signal processing. However, few approaches evaluate or present effective methods to attenuate impulsive and amplitude modulated (AM) noise, which are commonly found in on-site measurements. In this paper, a new PD denoising approach is presented, based on overcomplete dictionaries of wavelet families and sparse representations through the Basis Pursuit Denoising (BPD). This method was tested in the processing of impulsive and amplitude modulated (AM) noise, which are commonly found in on-site measured PD signals.

The Monte Carlo method to uncertainty calculation of the displacement measurement interferometry in FTIR spectrometry systems

Since it was developed, the Michelson interferometer underwent considerable improvements compared to its original design. Nowadays, the interferometer is widely used in systems where there is a need to measure accurately lengths of a few nanometers. The improvements are largely credit to a better modeling of the physical phenomena causing impact in the measurement result. This paper aims to develop the mathematical and statistical models that represent not only the physical laws, but also the measurement process, including all those quantities relevant to the determination of the uncertainty of the measurement result. An algorithm based on the Monte Carlo method was implemented to evaluate the uncertainty of the measurand taking into account the probability distributions of all relevant input quantities, propagated by means of the model proposed.

Improvement of System Quality in a Generalized Finite-Element Method Using the Discrete Curvelet Transform

The generalized finite-element method (GFEM) enriched by plane waves has been proved suitable to solve the 2-D Helmholtz equation. However, when the number of unknowns increases, some difficulties arise, such as bad condition number. Therefore, developing a pre-processing methodology to identify an appropriate number of degrees of freedom for each problem could be useful. This paper presents a systematic approach to determine the optimal number of plane waves for each problem to be used in GFEM solution. It is based on the analysis of the traditional FEM solution with a poor mesh resolution using the fast discrete curvelet transform. The results indicate that the adopted strategy can guarantee accurate and converging responses for GFEM in complex problems, along with remarkable reductions of the computational cost.