Критерії необхідної та достатньої кількості ітерацій фільтрації неперіодичних нестаціонарних сигналів багатоітераційними методами

Виконано аналіз ефективності відомих процедурно-орієнтованих критеріїв визначення необхідної кількості ітерацій фільтрації нестаціонарних неперіодичних сигналів багатоітераційним методом ковзного середнього із зростаючою шириною вікна фільтрації на прикладі імпульсів напруги на плазмоерозійному навантаженні та струму в ньому. Розглянуто дві основні групи критеріїв, які ґрунтуються на порівнянні сигналу чинної ітерації його фільтрації або з сигналом попередньої ітерації, або з еталонним сигналом, а також критерій, що певною мірою має властивості критеріїв обох цих груп. Показано низьку результативність та неуніверсальність відомих критеріїв. Запропоновано нові об’єктно-орієнтовані критерії необхідної та достатньої кількості ітерацій фільтрації нестаціонарних неперіодичних сигналів, адаптивні до вимог подальшої обробки сигналів та наведено аналіз їхньої ефективності.Произведен анализ эффективности известных процедурно-ориентированных критериев определения необхо-димого количества итераций фильтрации нестационарных непериодических сигналов многоитерационным методом скользящего среднего с возрастающей шириной окна фильтрации на примере импульсов напряжения на плазмоэрозионной нагрузке и тока в ней. Рассмотрены две основные группы критериев, которые основаны на сравнении сигнала на текущей итерации его фильтрации либо с сигналом на предыдущей итерации, либо с эталонным сигналом, а также критерий, который определенной мерой имеет свойства критериев обеих этих групп. Показано низкую результативность и неуниверсальность известных критериев. Предложены новые объектно-ориентированные критерии необходимого и достаточного количества итераций фильтрации нестационарных непериодических сигналов, адаптивные к требованиям дальнейшей обработки сигналов, и приведен анализ их эффективности.An analysis of efficiency of procedure-oriented criteria for determining the required number of filtration iterations of non-stationary non-periodic signals by the multi-iterative method of the moving average with an increasing width of the filtering window on an instance of pulses of voltage on the plasma-erosive load and of current in it had fulfilled. Two main groups of criteria are considered which are based on a comparison of the signal at the current iteration of its filtering either with the signal at the previous iteration or with a reference signal. Also criterion which has properties of criteria of both these groups is considered. The low effectiveness and nonuniversality of the known criteria is shown. New objectively-oriented criteria for the necessary and sufficient number of iterations of filtering non-stationary nonperiodic signals, adaptive to the requirements for further signal processing, are proposed and an analysis of their effectiveness had fulfilled

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced

Partial discharge denoising for power cables

Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising.Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising