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1

Discrete wavelet transform and energy distribution for effective bearing fault detection and analysis

Beldjaatit Chahinez, Sebbagh Toufik, Guentri Hocine

Przegląd Elektrotechniczny

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2023

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R. 99, nr 7

24--28

EN

Condition monitoring and problem diagnostics have drawn more attention recently in the industrial sector. One of the most crucial parts of rotating machinery are rolling-element bearings. Bearing faults are a common cause of machinery failures. To identify failing bearings early, vibration condition monitoring of rotating machinery has emerged as the preferred technique. Several signal analysis techniques can extract useful information from vibration data. The non-stationary analysis signals that are typically associated with machine defects cannot be handled by frequency-based approaches. Non-stationary signals are analyzed effectively by applying time-frequency techniques. The use of wavelet transform has increased in bearing monitoring research for the last 20 years to obtain correlated time-frequency information. This paper presents a discrete wavelet transform (DWT) and energy distribution-based bearing defect diagnostic technique. The "db3" wavelet form of DWT is used to decompose vibration signals under both normal and faulty (inner race-fault and outer race-fault) bearing conditions at various frequency ranges. Due to the default, the energy distribution for every decomposition level is calculated to detect which frequency band contains the harmonics. The results obtained from healthy and defective bearings are compared. The wavelet coefficient with the highest value of the energy distribution is employed in the Fourier analysis to pinpoint the site of the fault. The monitoring results demonstrate that the suggested approach is effective in finding and analyzing faults.

PL

Monitorowanie stanu i diagnostyka problemów przyciągnęły ostatnio więcej uwagi w sektorze przemysłowym. Jedną z najbardziej kluczowych części maszyn wirujących są łożyska toczne. Usterki łożysk są częstą przyczyną awarii maszyn. W celu wczesnej identyfikacji uszkodzonych łożysk, monitorowanie stanu wibracji maszyn wirujących stało się preferowaną techniką. Kilka technik analizy sygnału może wydobyć użyteczne informacje z danych o drganiach. Niestacjonarne sygnały analizy, które są zwykle związane z uszkodzeniami maszyn, nie mogą być obsługiwane przez podejścia oparte na częstotliwości. Sygnały niestacjonarne są skutecznie analizowane poprzez zastosowanie technik czasowoczęstotliwościowych. Zastosowanie transformaty falkowej wzrosło w badaniach nad monitorowaniem łożysk przez ostatnie 20 lat w celu uzyskania skorelowanej informacji czasowo-częstotliwościowej. W niniejszej pracy przedstawiono dyskretną transformatę falkową (DWT) oraz technikę diagnostyczną opartą na rozkładzie energii. Forma falkowa "db3" DWT jest używana do dekomponowania sygnałów drganiowych w warunkach łożyska zarówno normalnego, jak i wadliwego (wewnętrznego i zewnętrznego) w różnych zakresach częstotliwości. Ze względu na domyślność, rozkład energii dla każdego poziomu dekompozycji jest obliczany w celu wykrycia, które pasmo częstotliwości zawiera harmoniczne. Wyniki uzyskane z łożysk zdrowych i uszkodzonych są porównywane. Współczynnik falkowy o największej wartości rozkładu energii jest wykorzystywany w analizie Fouriera w celu określenia miejsca uszkodzenia. Wyniki monitorowania pokazują, że proponowane podejście jest skuteczne w wyszukiwaniu i analizie uszkodzeń.

2

An improved performance of support vector machine to classify EEG motor imagery based on differential asymmetry

Putranto Yulianto Tejo, Putra Oddy Virgantara, Hafidz Isa, Sardjono Arief, Hariadi Mochamad, Putranto Yulianto Tejo

Przegląd Elektrotechniczny

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2023

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R. 99, nr 6

196--203

EN

One challenge in EEG motor imaging is th e low signal-to-noise ratio of brain signals. Its emergence in the accurate rendition of brain signals varies significantly from person to person. Here, we propose a framework to classify tasks based on fusion features using a Support Vector Machine. Our features are acquired from Discrete Wavelet Transform and Empirical Mode Decomposition. Subsequently, the disparity between measurements of left and right brain signals was calculated. Our proposed work significantly improves accuracy from 83.29 % to 93.16 % compared to previous work.

PL

Jednym z wyzwań w obrazowaniu motorycznym EEG jest niski stosunek sygnału do szumu sygnałów mózgowych. Jego pojawienie się w dokładnym przekazywaniu sygnałów mózgowych różni się znacznie w zależności od osoby. Tutaj proponujemy ramy do klasyfikowania zadań w oparciu o funkcje fuzji przy użyciu maszyny wektorów nośnych. Nasze funkcje są uzyskiwane z dyskretnej transformacji falkowej i dekompozycji trybu empirycznego. Następnie obliczono rozbieżność między pomiarami sygnałów lewego i prawego mózgu. Nasza proponowana praca znacznie poprawia dokładność z 83,29% do 93,16% w porównaniu z poprzednią pracą.

3

MSCNN-Multi-Sensor Image Fusion Using Dual channel CNN

Trivedi Gargi J., Sanghavi Rajesh

Mathematica Applicanda

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2023

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Vol. 51, no. 2

165--182

EN

This paper describes an image fusion approach based on CNNs and DWT. According to the suggested method, First Each inputted image is decomposed into approximation coefficients and detail coefficients using DWT. The second step is to maximize the weights using CNN with detailed coefficients. Third, using maximum weight and max pooling, the combined detail images are produced. Fourth, an average pooling of the approximate coefficients is used to determine the final approximation coefficients. Lastly, Inverse DWT is then used to combine the detail and final approximation images to produce the final fused image. Experiments are carried out on four different fusion datasets. Different Quality checking metrics are used to analyze the data, and the results are then contrasted with more recent and usual fusion techniques. The result substantiates that the suggested technique performs better than the existing fusion methods. It is also appropriate for real-time applications due to the proposed method's reasonable computational time and simple yet efficient implementation.

PL

Artykuł dotyczy wielosensorowych konwolucyjnych sieci neuronowych (MS CNN) do fuzji obrazów w oparciu o konwolucyjne sieci neuronowe (CNN) i dyskretną transformację falkową (DWT). Zgodnie z sugerowaną metodą, najpierw każdy wprowadzony obraz jest rozkładany na współczynniki aproksymacji i współczynniki szczegółowości przy użyciu DWT. Drugim krokiem jest maksymalizacja wag za pomocą CNN ze szczegółowymi współczynnikami. W trzecim etapie, przy użyciu maksymalnej wagi i maksymalnego łączenia, tworzone są połączone szczegółowe obrazy. W czwartym etapie stosuje się średnią sumę przybliżonych współczynników w celu określenia ostatecznych współczynników przybliżenia. Na koniec stosuje się odwrotną DWT do łączenia obrazów szczegółowych i końcowych przybliżeń w celu uzyskania ostatecznego połączonego obrazu. Eksperymenty przeprowadzane są na czterech różnych zbiorach danych. Do analizy danych wykorzystuje się różne wskaźniki kontroli jakości, a następnie wyniki porównuje się z nowszymi i typowymi technikami łączenia. Wynik potwierdza, że sugerowana technika działa lepiej niż istniejące metody aglutynacji. Nadaje się również do zastosowań w czasie rzeczywistym ze względu na rozsądny czas obliczeń proponowanej metody oraz prostą, ale efektywną implementację.

4

Fault detection in robots based on discrete wavelet transformation and eigenvalue of energy

Ouarhlent Saloua, Terki Nadjiba, Hamiane Madina, Dahmani Habiba

Diagnostyka

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2023

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Vol. 24, No. 4

art. no. 2023407

EN

This article addresses the problem of fault detection in robot manipulator systems. In the production field, online detection and prevention of unexpected robot stops avoids disruption to the entire manufacturing line. A number of researchers have proposed fault diagnosis architectures for electrical systems such as induction motor, DC motor, etc..., utilising the technique of discrete wavelet transform. The results obtained from the use of this technique in the field of diagnosis are very encouraging. Inspired by previous work, The objective of this paper is to present a methodology that enables accurate fault detection in the actuator of a two-degree of freedom robot arm to avoid system performance degradation. A partial reduction in joint torque constitutes the actuator fault, resulting in a deviation from the desired end-effector motion. The actuator fault detection is carried out by analysing the torques signals using the wavelet transform. The stored energy at each level of the transform contains information which can be used as a fault indicator. A Matlab/Simulink simulation of the manipulator robot demonstrates the effectiveness of the proposed technique.

5

Hybrid deep learning method for detection of liver cancer

Deshmukh Sunita P., Choudhari Dharmaveer, Amalraj Shankar, Matte Pravin N.

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 2

151--165

EN

Liver disease refers to any liver irregularity causing its damage. There are several kinds of liver ailments. Benign growths are rarely life threatening and can be removed by specialists. Liver malignant tumor is leading causes of cancer death. Identifying malignant growth tissue is a troublesome and tedious task. There is significantly less information and statistical analysis presented related to cholangiocarcinoma and hepatoblastoma. This research focuses on the image analysis of these two types of cancer. The framework’s performance is evaluated using 2871 images, and a dual hybrid model is used to accomplish superb exactness. The aftereffects of both neural networks are sent into the result prioritizer that decides the most ideal choice for image arrangement. The relevance of elements appears to address the appropriate imaging rules for each class, and feature maps matching the original picture voxel features. The significance of features represents the most important imaging criteria for each class. This deep learning system demonstrates the concept of illuminating elements of a pre-trained deep neural network’s decision-making process by an examination of inner layers and the description of attributes that contribute to predictions.

6

Damage localization in truss girders by an application of the discrete wavelet transform

Knitter-Piątkowska Anna, Kawa Olga, Guminiak Michał Jan

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 1

art. no. e144581

EN

The paper demonstrates the potential of wavelet transform in a discrete form for structural damage localization. The efficiency of the method is tested through a series of numerical examples, where the real flat truss girder is simulated by a parameterized finite element model. The welded joints are introduced into the girder and classic code loads are applied. The static vertical deflections and rotation angles of steel truss structure are taken into consideration, structural response signals are computed at discrete points uniformly distributed along the upper or lower chord. Signal decomposition is performed according to the Mallat pyramid algorithm. The performed analyses proved that the application of DWT to decompose structural response signals is very effective in determining the location of the defect. Evident disturbances of the transformed signals, including high peaks, are expected as an indicator of the defect existence in the structure. The authors succeeded for the first time in the detection of breaking the weld in the truss node as well as proved that the defect can be located in the diagonals.

7

A new super resolution Faster R-CNN model based detection and classification of urine sediments

Avci Derya, Sert Eser, Dogantekin Esin, Yildirim Ozal, Tadeusiewicz Ryszard, Plawiak Pawel

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

58--68

EN

The diagnosis of urinary tract infections and kidney diseases using urine microscopy images has gained significant attention of medical community in recent years. These images are usually created by physicians’ own rule of thumb manually. However, this manual urine sediment analysis is usually labor-intensive and time-consuming. In addition, even when physicians carefully examine an image, an erroneous cell recognition may occur due to some optical illusions. In order to achieve cell recognition in low-resolution urine microscopy images with a higher level of accuracy, a new super resolution Faster Region-based Convolutional Neural Network (Faster R-CNN) method is proposed. It aims to increase resolution in low-resolution urine microscopy images using self-similarity based single image super resolution which was used during the pre-processing. Denoising based Wiener filter and Discrete Wavelet Transform (DWT) are used to de-noise high resolution images, respectively, to increase the level of accuracy for image recognition. Finally, for the feature extraction and classification stages, AlexNet, VGFG16 and VGG19 based Faster R-CNN models are used for the recognition and detection of multi-class cells. The model yielded accuracy rates are 98.6%, 96.4% and 96.2% respectively.

8

On site processing of video stream for mapping traffic parameters

Pamuła Wiesław, Kłos Marcin Jacek

Zeszyty Naukowe. Transport / Politechnika Śląska

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2022

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z. 117

175--189

EN

Traffic surveillance provides crucial data for the operation of intelligent transportation systems. The growing number of cameras in the transport system poses a problem for the efficient processing of surveillance data. Processing of video data for extracting traffic parameters is usually done using image processing methods and requires substantial processing resources. An alternative way is to transform the video stream and map the traffic parameters using the obtained transform coefficients. Spatiotemporal wavelet transform of the video stream contents, using filter banks, is proposed for mapping traffic parameters. Performed tests prove good resilience to illumination changes of road scenes. Mapping errors are smaller than in the case of the commonly used video detectors at sites on multilane roads with low to moderate traffic load.

9

Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms

Puchala Dariusz, Stokfiszewski Kamil, Wieloch Kamil

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 1

art. no. e139393

EN

Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.

10

Characterization of the anteroposterior center of pressure in upright position in type 2 diabetics with peripheral diabetic neuropathy and paired healthy controls

Toloza-Cano Daissy Carola, Zequera Martha, González Gustavo Hernando Castro

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

306--315

EN

Peripheral diabetic neuropathy (PDN) is a complication of type 2 diabetes (T2DM) that impairs posture control and increases the risk of falling. The aim of this study was to characterize the anteroposterior center of pressure (COP-AP) in the time and the frequency domains in the T2DM/PDN group in relation to the control group. To that end we: (1) evaluated the efficacy of using both linear and non-linear discrete wavelet transform (DWT) analyses to evaluate oscillation patterns in the anteroposterior center of pressure (COP-AP) in the bipedal position in terms of time and frequency and (2) established input parameters for a model for predicting the risk of falling. This study included an experimental sample of 30 people with T2DM/PDN matched by gender, age, weight and height with a control sample of 30 healthy individuals. Unreported techniques for analyzing the COP-AP literature were assessed for their capacity to model patient bodily stability in the proprioceptive, visual and vestibular systems. To measure COP-AP, five tests were performed under different conditions as outlined in the Romberg Test using the ‘‘PEDAR System’’ for measuring plantar pressure. DWTs were used to calculate excursion parameters, average speeds, range, RMS values, the average maximum and minimum amplitude, power spectral densities and energy percentages in 11 frequency bands (D1 to D10 and A10). There were significant differences between the two study groups in terms of the calculated linear parameters ( p < 0.05). Using linear and non-linear DWT analyses, a preliminary characterization of COP-AP patterns was achieved. DWT could be used alongside linear analysis to determine the effect changes in these systems have on postural oscillation in people with T2DM.

11

Artifact removal from EEG signals recorded in non-restricted environmentArtifact removal from EEG signals recorded in non-restricted environment

Jamil Zainab, Jamil Afshan, Majid Muhammad

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

503--515

EN

Electroencephalography (EEG) signals are always accompanied by endogenous and exogenous artifacts. Research carried out in the past few years focused on EEG artifact removal considered EEG signals recorded in a restricted lab environment. Considering the importance of EEG in daily life activities, no definitive approach is presented in removing blink artifacts from non-restricted EEG recordings. In this paper, a new supervised artifact removal method is proposed that classifies EEG chunks having eye movements and then utilizes independent component analysis and discrete wavelet transform to eliminate the ocular artifacts. The EEG data is acquired from 29 subjects in a non-restricted environment where the subject has to watch videos while walking and giving gestures and facial expressions. Thirteen morphological features are extracted from the recorded EEG signals to classify chunks with eye movements. The EEG chunks with eye movements are further processed to remove noise without distorting the morphology of signals. The proposed method is tested for eye movements and shows an improved performance in terms of correlation, mutual information, phase difference, and computational time over unsupervised modified multi-scale sample entropy and kurtosis, and wavelet enhanced independent component analysis based approaches. Moreover, the computed values of statistical parameters including sensitivity and specificity show the robustness of the proposed scheme.

12

High impedance fault detection in radial distribution network using discrete wavelet transform technique

Suliman Mohammed Yahya, Alkhayyat Mahmood Taha

Archives of Electrical Engineering

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2021

|

Vol. 70, nr 4

873--886

EN

Detecting high impedance faults (HIFs) is one of the challenging issues for electrical engineers. This type of fault occurs often when one of the overhead conductors is downed and makes contact with the ground, causing a high-voltage conductor to be within the reach of personnel. As the wavelet transform (WT) technique is a powerful tool for transient analysis of fault signals and gives information both on the time domain and frequency domain, this technique has been considered for an unconventional fault like high impedance fault. This paper presents a new technique that utilizes the features of energy contents in detail coefficients (D4 and D5) from the extracted current signal using a discrete wavelet transform in the multiresolution analysis (MRA). The adaptive neurofuzzy inference system (ANFIS) is utilized as a machine learning technique to discriminate HIF from other transient phenomena such as capacitor or load switching, the new protection designed scheme is fully analyzed using MATLAB feeding practical fault data. Simulation studies reveal that the proposed protection is able to detect HIFs in a distribution network with high reliability and can successfully differentiate high impedance faults from other transients.

13

Wykrywanie uszkodzeń w konstrukcjach inżynierskich przy zastosowaniu metody dyskretnej transformacji falkowej

Knitter-Piątkowska Anna, Guminiak Michał

Przegląd Budowlany

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2020

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R. 91, nr 11

61--64

PL

Badania związane z wykrywaniem uszkodzeń i osłabień elementów konstrukcyjnych stanową bardzo ważny element kompleksowej analizy budowli inżynierskich. W analizie identyfikacji uszkodzeń wiodącą rolę odgrywają tzw. metody nieniszczące, które pozwalają dostatecznie precyzyjnie zlokalizować powstałe uszkodzenia. Prezentowana praca poświęcona jest zastosowaniu dyskretnej transformacji falkowej w procesie lokalizacji uszkodzeń konstrukcji. Dowolne uszkodzenie, np. w postaci lokalnego osłabienia sztywności konstrukcji (pęknięcia), jest przyczyną zaburzenia w rejestrowanym sygnale odpowiedzi - ugięciu, deformacji przekroju lub np. przyspieszeniu wybranego punktu konstrukcji. Zaburzenie sygnału jest na tyle małe, że dopiero jego przetworzenie za pomocą analizy falkowej pozwala zlokalizować miejsce uszkodzenia. Zaletą przedstawionej procedury jest wykorzystanie wyłącznie sygnału odpowiedzi rzeczywistej konstrukcji uszkodzonej. Przedstawiono krótki przegląd dotychczasowych analiz konstrukcji płytowych (płyt cienkich).

EN

Research related to the detection of damage and weakening of structural elements is a very important element of a comprehensive analysis of engineering structures. In the analysis of damage identification, the leading role is played by the so-called non-destructive methods that allow for sufficiently precise localization of the damage. The presented work is devoted to the application of the discrete wavelet transformation (DWT) to the process of identification and localization damages in structures. Any damage, e.g. in the form of a local weakening of the structure stiffness (cracks), causes disturbances in the recorded response signal - deflection, deformation of the cross-section or e.g. acceleration of a selected point of the structure. However, the signal disturbance is so small that only its processing by means of wavelet analysis allows to locate the damage site. The advantage of the presented procedure is the use of the response signal only of the real - damaged structure. The presented work is an overview of the results obtained so far. The slabs were analyzed as the basic surface structural systems that form the building structure.

14

A novel privacy-supporting 2-class classification technique for brain MRI images

Devi Swagatika, Sahoo Manmath Narayan, Bakshi Sambit

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1022--1035

EN

Developing automated Computer Aided Diagnosis (CAD) framework for assisting radiologists in a fast and effective classification of brain Magnetic Resonance (MR) images is of great importance, given plausible usage of Electronic Health Records (EHR) in healthcare system. This work aims at proposing two novel privacy supporting classifiers for automatic segregation of brain MR images. To ensure privacy, our article employs a spatial steganographic approach to hide patients sensitive health information in brain images itself. Proposed methods employ Discrete Wavelet Transform (DWT) for extracting relevant features from original and stego images. Subsequently, Symmetrical Uncertainty Ranking (SUR) and Probabilistic Principal Components Analysis (PPCA) are used to obtain a reduced feature vector for Support Vector Machine (SVM) and Filtered Classifier (FC) respectively. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate this work, the proposed schemes are experimented on both original and stego brain MR images and are compared against eight state-of-the-art classification techniques with respect to six standard parameters. The results reveal that the proposed techniques are robust and scalable with respect to the size of the datasets. Moreover, it is concluded that applying steganographic algorithm on brain MR images yield equally satisfactory classification rate.

15

The use of hybrid methods for change points and trends detection in rainfall series of northern Algeria

Zerouali Bilel, Chettih Mohamed, Abda Zaki, Mesbah Mohamed, Djemai Mohammed

Acta Geophysica

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2020

|

Vol. 68, no. 5

1443--1460

EN

The aim of this research is to assess relatively new hybrid methods for changes points and trends detection on rainfall series: Dynamic Programming Bayesian Change Point Approach (BA), Şen’s innovative trend method (ITM) and its double (D-ITM) and triple (T-ITM) version using the multi-scale analysis of the discrete wavelet transform (DWT) as a coupling method. Three representatives rainfall stations of northern Algeria were analysed at annual scale during the period 1920–2011. Moreover, correlation and spectral analysis (CSA) was applied for periodicity analysis. The CSA indicates the dominance of interannual to multidecadal rainfall periodicity fuctuations (2-years, 5-years and 20-years) characterising long term structured processes. Moreover, an abrupt downward trend with signifcant probability was detected from the 1970s with a relatively wet period between the periods 1950–1970 and 2001–2011. The latter is observed in particular in the central and eastern stations, well-explained by the BA-DWT. The results showed that the comparison results from diferent modelling approaches found that the hybrid models (BA-DWT, ITM-DWT, D-ITM-DWT, T-ITM-DWT) often perform better than the conventional approach (BA, ITM, D-ITM, T-ITM), where the computation time is very reasonable. The analysis revealed that information stemming from discrete wavelet spectrums signifcantly increased the accuracy of the methods for detecting hidden change points and trends.

16

Wpływ parametrów banku filtrów na skuteczność redukcji szumu w sygnale pomiarowym przy zastosowaniu algorytmu dyskretnej transformacji falkowej

Dróżdż Łukasz, Roj Jerzy

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

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2019

|

Nr 66

11--14

PL

W artykule przedstawiono metodę usuwania szumu z sygnału pomiarowego bazującą na zastosowaniu dyskretnej transformacji falkowej. Przeprowadzone badania miały na celu wskazanie wpływu parametrów banku filtrów na skuteczność redukcji szumu w sygnale pomiarowym. Badania obejmowały ustalenie wpływu rodzaju falki oraz liczby poziomów dekompozycji na skuteczność algorytmu usuwania szumu. Prawidłowy dobór tych parametrów jest kluczowy do prawidłowego działania algorytmu.

EN

Paper describes signal denoising algorithm based on discrete wavelet transform. Research includes search how filterbank parameters influences on signal denoising. Paper describes how decomposition count and wavelet type influences on denoising process. Right choice of this parameters is very important to algorithm performs well. The wavelet transform algorithm is a tool enabling the analysis of signals by presenting them using a scaled and time-shifted function called a „mother wavelet”. Signal analysis requires decomposition process performed by filter banks witch parameters depends on mother wavelet and count of decomposition iterations. Presented wavelet signal denoising technique focuses on transform coefficient correction based on estimated noise parameters. This correction can be performed in many ways, depending on used strategy. Paper presents hard thresholding algorithm based on adaptive noise parameters estimation. According to simulation results the mother wavelet choice is not as important, as choice of right decompositions level count. Presented method provides better results than other common methods such Gaussian filter or average filter.

17

Improved reference image encryption methods based on 2k correction in the integer wavelet domain

Tuncer Turker, Dogan Sengul, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 4

817--829

EN

Many visually meaningful image encryption (VMIE) methods have been proposed in the literature using reference encryption. However, the most important problems of these methods are low visual quality and blindness. Owing to the low visual quality, the pre-encrypted image can be analyzed simply from the reference image and, in order to decrypt nonblind methods, users should use original reference images. In this paper, two novel reference image encryption methods based on the integer DWT (discrete wavelet transform) using 2k correction are proposed. These methods are blind and have high visual quality, as well as short execution times. The main aim of the proposed methods is to solve the problem of the three VMIE methods existing in the literature. The proposed methods mainly consist of the integer DWT, pre-encrypted image embedding by kLSBs (k least significant bits) and 2k correction. In the decryption phase, the integer DWT and preencrypted image extraction with the mod operator are used. Peak signal-to-noise ratio (PSNR) measures the performances of the proposed methods. Experimental results clearly illustrate that the proposed methods improve the visual quality of the reference image encryption methods. Overall, 2k correction and kLSBs provide high visual quality and blindness.

18

DWT-PSD extraction feature for defect diagnosis of small wind generator

Noureddine Lahcène, Noureddine Mustapha, Hafaifa Ahmed, Kouzou Abdellah

Diagnostyka

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2019

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Vol. 20, No. 3

45--52

EN

In this paper, the ability to detect broken rotor bar (BRB) defects in a small renewable energy system (based on a squirrel cage induction generator (SCIG)) by the digital signal processing of captured phase currents, is presented. The new approach proposed in this study is a combination of two techniques. The first technique is a discrete wavelet transform (DWT) by the decomposition of the phase current signal in multilevel frequency bands. This is performed with the analysis of some selected approximations and/or details, which contain both the lower and upper sideband components presenting the characteristic frequency of the BRB fault. The second technique is power spectral density (PSD) analysis. This approach provides the ability to optimize the diagnosis of rotor defects in electrical generators. The results obtained by the proposed DWT-PSD approach are proved and improved by comparing them with the results of the PSD analysis, obtained from the original phase current signal delivered by the 5.7-kW squirrel cage induction generator, based on a small wind energy conversion system.

19

Mechanical fault detection in rotating electrical machines using MCSA-FFT and MCSA-DWT techniques

Bessous N., Sbaa S., Megherbi A. C.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

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Vol. 67, nr 3

571--582

EN

This paper presents mechanical fault detection in squirrel cage induction motors (SCIMs) by means of two recent techniques. More precisely, we have analyzed the rolling element bearing (REB) faults in SCIM. Rolling element bearing faults constitute a major problem among different faults which cause catastrophic damage to rotating machinery. Thus early detection of REB faults in SCIMs is of crucial importance. Vibration analysis is among the key concepts for mechanical vibrations of rotating electrical machines. Today, there is massive competition between researchers in the diagnosis field. They all have as their aim to replace the vibration analysis technique. Among them, stator current analysis has become one of the most important subjects in the fault detection field. Motor current signature analysis (MCSA) has become popular for detection and localization of numerous faults. It is generally based on fast Fourier transform (FFT) of the stator current signal. We have detailed the analysis by means of MCSA-FFT, which is based on the stator current spectrum. Another goal in this work is the use of the discrete wavelet transform (DWT) technique in order to detect REB faults. In addition, a new indicator based on the MCSA-DWT technique has been developed in this study. This new indicator has the advantage of expressing itself in the quantity and quality form. The acquisition data are presented and a comparative study is carried out between these recent techniques in order to ensure a final decision. The proposed subject is examined experimentally using a 3 kW squirrel cage induction motor test bed.

20

System Identification and Model Predictive Control of the Chatter Phenomenon in Turning Process

Karimipour Atiyeh, Emami Mohsen

Advances in Science and Technology. Research Journal

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2019

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Vol. 13, no 3

217--228

EN

Chatter is a series of unwanted and extreme vibrations which frequently happens during different machining processes and impose variety of adverse effects on the machine-tool and surface finish. Chatter has two main types namely forced-chatter and self-existed chatter. The forced-chatter has an external cause; however, self-exited chatter has no external stimuli, rather it is created due to the phase difference between the previous and current waves on the surface of the workpiece. Due to the self-generative nature of this type of chatter, its recognition and prevention is much more difficult. For preventing self-exited chatter its model should be available first. The chatter is usually simulated as a one degree of freedom mass-spring-damper model with unknown parameters that they should be determined somehow. In this paper, the parameters of the tool equation of motion i.e. mass, damping, and stiffness coefficients of the system are predicted through a wavelet-based method online, and then based on the achieved parameters, the system is controlled via Model Predictive Control (MPC) approach. For the validation, the algorithm is applied to 25 different experimental tests in which the acceleration of the tool and cutting force are measured via an accelerometer and a dynamometer. By investigation of the SLDs generated by the predicted parameters, the presented system identification method is validated. Also, it is shown that the chatter vibration is completely restrained by means of MPC. For investigation of the MPC performance, MPC algorithm is compared with PID controller and simulations has indicated a much stronger performance of MPC rather than PID controller in terms of vibration attenuation and control effort.