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1

Parkinson disease detection using energy direction features based on EMD from voice signal

Zhang Tao, Zhang Yajuan, Sun Hao, Shan Haoran

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

127--141

EN

Voice disorders are one of the incipient symptoms of Parkinson's disease (PD). Recent studies have shown that approximately 90% of PD patients suffer from vocal disorders. Therefore, it is significant to extract pathological information on the voice signals to detect PD. In this paper, a feature, named energy direction features based on empirical mode decomposition (EDF-EMD), is proposed to show the different characteristics of voice signals between PD patients and healthy subjects. Firstly, the intrinsic mode functions (IMFs) were obtained through the decomposition of voice signals by EMD. Then, the EDF is obtained by calculating the directional derivatives of the energy spectrum of each IMFs. Finally, the performance of the proposed feature is verified on two different datasets: dataset-Sakar and dataset-CPPDD. The proposed approach shows the best average resulting accuracy of 96.54% on dataset-Sakar and 92.59% on dataset-CPPDD. The results demonstrate that the method proposed in this paper is promising in the field of PD detection.

2

Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm

Bavkar Sandeep, Iyer Brijesh, Deosarkar Shankar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

83--96

EN

Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging task. The paper reports a new methodology for the detection of optimal channels for alcoholism analysis using EEG data. The proposed technique employs the Empirical Mode Decomposition (EMD) technique to extract the amplitude and frequency modulated bandwidth features from the Intrinsic Mode Function (IMF) and ensemble subspace K-NN as a classifier to classify alcoholics and normal. The optimum channels are selected, using a harmony search algorithm. The fitness value of discrete binary harmony search (DBHS) optimization algorithms is calculated using accuracy and sensitivity achieved by the ensemble subspace K-Nearest Neighbor classifier. Experimental outcomes indicate that the optimal channel selected by the harmony search algorithm has biological inference related to the alcoholic subject. The proposed approach reports a classification accuracy of 93.87%, with only 12 detected EEG channels.

3

Gene prediction by the noise-assisted MEMD and wavelet transform for identifying the protein coding regions

Zheng Qian, Chen Tao, Zhou Wenxiang, Xie Lei, Su Hongye

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

196--210

EN

The analysis of protein coding regions of DNA sequences is one of the most fundamental applications in bioinformatics. A number of model-independent approaches have been developed for differentiating between the protein-coding and non-protein-coding regions of DNA. However, these methods are often based on univariate analysis algorithms, which leads to the loss of joint information among four nucleotides of DNA. In this article, we introduce a method on basis of the noise-assisted multivariate empirical mode decomposition (NA-MEMD) and the modified Gabor-wavelet transform (MGWT). The NA-MEMD algorithm, as a multivariate analysis tool, is utilized to reconstruct the numerical analyzed sequence since it enables a matched-scale decomposition across all variables and eliminates the mode mixing. By virtues of NA-MEMD, the MGWT method achieves a stable improvement on the general identification performance. We compare our method with other Digital Signal Processing (DSP) methods on two representative DNA sequences and three benchmark datasets. The results reveal that our method can enhance the spectra of the analyzed sequences, and improve the robustness of MGWT to different DNA sequences, thus obtaining higher identification accuracies of protein coding regions over other applied methods. In addition, another comparative experiment with the model-dependent method (AUGUSTUS) on the recently proposed benchmark dataset G3PO verifies the superiority of model-independent methods (especially NA-MEMD-MGWT) for identifying coding regions of the poor-quality DNA sequences.

4

Epileptic seizure prediction using scalp electroencephalogram signals

Usman Syed Muhammad, Khalid Shehzad, Bashir Zafar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

211--220

EN

Epilepsy is a brain disorder in which patients undergo frequent seizures. Around 30% of patients affected with epilepsy cannot be treated with medicines/surgical procedures. Abnormal activity, known as the preictal state starts few minutes before the seizure actually occurs. Therefore, it may be possible to deliver medication prior to the occurrence of a seizure if initiation of the preictal state can predicted before the seizure onset. We propose an epileptic seizure prediction method that predicts the preictal state before the seizure onset using electroencephalogram (EEG) monitoring of brain activity. It involves three steps including preprocessing of EEG signals, feature extraction classification of preictal and interictal states. In our proposed method, we have used (i) Empirical model decomposition to remove noise from the EEG signals and Generative Adversarial Networks to generate preictal samples to deal with the class imbalance problem; (ii) Automated features have been extracted with three layer Convolutional Neural Networks and (iii) Classification between preictal and interictal states is done with Long Short Term Memory units. In this study, we have used CHBMIT dataset of scalp EEG signals and have validated our proposed method on 22 subjects of dataset. Our proposed seizure prediction method is able to achieve 93% sensitivity and 92.5% specificity with average time of 32 min to predict the seizure's onset. Results obtained from our method have been compared with recent state-of-the-art epileptic seizure prediction methods. Our proposed method performs better in terms of sensitivity, specificity and average anticipation time.

5

An improved MAMA-EMD for the automatic removal of EOG artifacts

Li Mingai, Zhang Yuanyuan

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1182--1196

EN

The separation of electrooculogram (EOG) and electroencephalogram (EEG) is a potential problem in brain-computer interface (BCI). Especially, it is necessary to accurately remove EOG, as a disturbance, from the measured EEG in brain disease diagnosis, EEG-based rehabilitation systems, etc. Due to the interaction between the eye and periocular musculature, a multipoint spike is often produced in EEG for each ocular activity. Masking-aided minimum arclength empirical mode decomposition (MAMA-EMD) was developed to robustly decompose time series with impulse-like noise. However, the decomposition performance of MAMA-EMD was limited in the case of one impulse with multiple contiguous spike points. In this paper, MAMA-EMD was improved (called IMAMA-EMD) by supplementing the minimum arclength criterion, and it was combined with kernel independent component analysis (KICA), yielding an automatic EOG artifact removal method, denoted as KIIMME. The multi-channel contaminated EEG signals were separated into several independent components (ICs) by KICA. Then, IMAMA-EMD was applied to the EOG-related ICs decomposition to generate a set of inherent mode functions (IMFs), the low frequency ones, which have higher correlation with EOG components, were removed, and the others were employed to construct ‘clean’ EEG. The proposed KIIMME was evaluated and compared with other methods on semisimulated and real EEG data. Experimental results demonstrated that IMAMA-EMD effectively eliminated the influence of multipoint spike on sifting process, and KIIMME improved the removal accuracy of EOG artifacts from EEG while retaining more useful neural data. This improvement is of great significance to research on brain science as well as BCI.

6

An automated skin melanoma detection system with melanoma-index based on entropy features

Cheong Kang Hao, Tang Kenneth Jian Wei, Zhao Xinxing, Koh Joel En Wei, Faust Oliver, Gururajan Raj, Ciaccio Edward J., Rajinikanth V., Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

997--1012

EN

Skin melanoma is a potentially life-threatening cancer. Once it has metastasized, it may cause severe disability and death. Therefore, early diagnosis is important to improve the conditions and outcomes for patients. The disease can be diagnosed based on Digital-Dermoscopy (DD) images. In this study, we propose an original and novel Automated Skin-Melanoma Detection (ASMD) system with Melanoma-Index (MI). The system incorporates image pre-processing, Bi-dimensional Empirical Mode Decomposition (BEMD), image texture enhancement, entropy and energy feature mining, as well as binary classification. The system design has been guided by feature ranking, with Student’s t-test and other statistical methods used for quality assessment. The proposed ASMD was employed to examine 600 benign and 600 DD malignant images from benchmark databases. Our classification performance assessment indicates that the combination of Support Vector Machine (SVM) and Radial Basis Function (RBF) offers a classification accuracy of greater than 97.50%. Motivated by these classification results, we also formulated a clinically relevant MI using the dominant entropy features. Our proposed index can assist dermatologists to track multiple information-bearing features, thereby increasing the confidence with which a diagnosis is given.

7

Parkinson disease prediction using intrinsic mode function based features from speech signal

Karan Biswajit, Sahu Sitanshu Sekhar, Mahto Kartik

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

249--264

EN

Parkinson's disease (PD) is a progressive neurological disorder prevalent in old age. Past studies have shown that speech can be used as an early marker for identification of PD. It affects a number of speech components such as phonation, speech intensity, articulation, and respiration, which alters the speech intelligibility. Speech feature extraction and classification always have been challenging tasks due to the existence of non-stationary and discontinuity in the speech signal. In this study, empirical mode decomposition (EMD) based features are demonstrated to capture the speech characteristics. A new feature, intrinsic mode function cepstral coefficient (IMFCC) is proposed to efficiently represent the characteristics of Parkinson speech. The performances of proposed features are assessed with two different datasets: dataset-1 and dataset-2 each having 20 normal and 25 Parkinson affected peoples. From the results, it is demonstrated that the proposed intrinsic mode function cepstral coefficient feature provides superior classification accuracy in both data-sets. There is a significant increase of 10–20% in accuracy compared to the standard acoustic and Mel-frequency cepstral coefficient (MFCC) features.

8

Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks

Altuve Miguel, Lizarazo Paula, Villamizar Javier

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

901--909

EN

The recognition of human activities is a topic of great relevance due to its wide range of applications. Different approaches have been proposed to recognize human activities, ranging from the comparison of signals with thresholds to the application of deep and machine learning techniques. In this work, the classification of six human activities (walking, walking downstairs, walking upstairs, standing, sitting, and lying down) is performed using bidirectional LSTM networks that exploit intrinsic mode function (IMF) representation of inertial signals. Records with inertial signals (accelerometer and gyroscope) of 2.56 s, available at the UCI Machine Learning Repository, were collected from 30 subjects using a smartphone. First, inertial signals were standardized to take them to the same scale and were decomposed into IMF using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). IMF were then segmented (split) into nine segments of 1.28 s with 12.5% overlap and introduced to a first network with four outputs to identify the dynamic activities and the statics as a single class called ‘‘statics’’, giving 98.86% accuracy. Then, the non-segmented IMF of the records assigned to the statics class were introduced to a second network to classify their three activities, giving an accuracy of 88.46%. In total, 92.91% accuracy was obtained to classify the six human activities. This performance is because ICEEMDAN allowed the extraction of information that was embedded in the signal, and the segmentation of the IMF allowed the network to discriminate between static and dynamic activities.

9

Detection of SSVEP based on empirical mode decomposition and power spectrum peaks analysis

Antelis Javier M., Rivera Camilo A., Galvis Eduard, Ruiz-Olaya Andres F.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1010--1021

EN

Steady-state visual evoked potential (SSVEP) based brain–computer interfaces have been widely studied because these systems have potential to restore capabilities of communication and control of disable people. Identifying target frequency using SSVEP signals is still a great challenge due to the poor signal-to-noise ratio of these signals. Commonly, this task is carried out with detection algorithms such as bank of frequency-selective filters and canonical correlation analysis. This work proposes a novel method for the detection of SSVEP that combines the empirical mode decomposition (EMD) and a power spectral peak analysis (PSPA). The proposed EMD+PSPA method was evaluated with two EEG datasets, and was compared with the widely used FB and CCA. The first dataset is freely available and consists of three flickering light sources; the second dataset was constructed and consists of six flickering light sources. The results showed that proposed method was able to detect SSVEP with high accuracy (93.67 ± 9.97 and 78.19 ± 23.20 for the two datasets). Furthermore, the detection accuracy results achieved with the first dataset showed that EMD+PSPA provided the highest detection accuracy (DA) in the largest number of participants (three out of five), and that the average DA across all participant was 93.67 ± 9.97 which is 7% and 4% more than the average DA achieved with FB and CCA, respectively.

10

Tests of basic voice stress detection techniques

Staroniewicz Piotr

Vibrations in Physical Systems

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2019

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Vol. 30, nr 1

art. no. 2019133

EN

The modern speech processing techniques enable new possibilities of potential applications. Besides speech and speaker recognition, also the information about speakers’ physical condition, emotional state or stress can be detected in speech signal. Since emotional stress can occur during deception, its detection in speech could be used for law or security services. The paper presents the comparative tests of two voice stress detection techniques: one based on trials of microtremors detection relying on an iterative EMD method (Empirical Mode Decomposition) and the second one based on the statistical analysis of fundamental frequency and MFCC parameters. The preliminary tests were carried on the group of 12 speakers (6 males and 6 females) answering yes/no to the list of a few dozen personal questions. The presented research revealed the speakers’ very high personal influence on the obtained results.

11

Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings

Souto Maior Caio Bezerra, das Chagas Moura Márcio, Lins Isis Didier

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 4

610--618

EN

The useful life time of equipment is an important variable related to system prognosis, and its accurate estimation leads to several competitive advantage in industry. In this paper, Remaining Useful Lifetime (RUL) prediction is estimated by Particle Swarm optimized Support Vector Machines (PSO+SVM) considering two possible pre-processing techniques to improve input quality: Empirical Mode Decomposition (EMD) and Wavelet Transforms (WT). Here, EMD and WT coupled with SVM are used to predict RUL of bearing from the IEEE PHM Challenge 2012 big dataset. Specifically, two cases were analyzed: considering the complete vibration dataset and considering truncated vibration dataset. Finally, predictions provided from models applying both pre-processing techniques are compared against results obtained from PSO+SVM without any pre-processing approach. As conclusion, EMD+SVM presented more accurate predictions and outperformed the other models.

PL

Okres użytkowania sprzętu jest ważną zmienną związaną z prognozowaniem pracy systemu, a możliwość jego dokładnej oceny daje zakładom przemysłowym znaczną przewagę konkurencyjną. W tym artykule pozostały czas pracy (Remaining Useful Life, RUL) szacowano za pomocą maszyn wektorów nośnych zoptymalizowanych rojem cząstek (SVM+PSO) z uwzględnieniem dwóch technik przetwarzania wstępnego pozwalających na poprawę jakości danych wejściowych: empirycznej dekompozycji sygnału (Empirical Mode Decomposition, EMD) oraz transformat falkowych (Wavelet Transforms, WT). W niniejszej pracy, EMD i falki w połączeniu z SVM wykorzystano do prognozowania RUL łożyska ze zbioru danych IEEE PHM Challenge 2012 Big Dataset. W szczególności, przeanalizowano dwa przypadki: uwzględniający kompletny zestaw danych o drganiach oraz drugi, biorący pod uwagę okrojoną wersję tego zbioru. Prognozy otrzymane na podstawie modeli, w których zastosowano obie techniki przetwarzania wstępnego porównano z wynikami uzyskanymi za pomocą PSO + SVM bez wstępnego przetwarzania danych. Wyniki pokazały, że model EMD + SVM generował dokładniejsze prognozy i tym samym przewyższał pozostałe badane modele.

12

Gear crack detection using residual signal and empirical mode decomposition

Selami S., Mecibah M. S., Debbah Y., Boukelia T. E.

Mechanics and Mechanical Engineering

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2018

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Vol. 22, nr 4

1133--1144

EN

Diagnosis of gearbox defects at an early stage is very important to avoid catastrophic failures. This article presents experimental results of tests made to evaluate the cracks of the cylindrical gears of a transfer case under advanced test conditions. For the diagnosis of a gearbox, various signal processing techniques are mainly used for the vibration study of the gears, such as: Fast Fourier Transform, synchronous time average, and time-based wavelet transformation, etc. Various methods can be found in the literature which can be used to calculate the residual signal (RS), however, in this paper, we suggest a new method combined empirical mode decomposition (EMD) technique with RS for detection of the crack gear. In order to extract the associated defect characteristics of the transfer box vibration signals, the EMD has been performed. The results show the effectiveness of the EMD method in the evaluation of tooth cracking in spur gears. This effectiveness can be proved by the obtained results of the experimental tests, which were presented and carried out on a test rig equipped with a transfer box.

13

Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique

Kumar S., Panigrahy D., Sahu P. K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

297--312

EN

In this paper, the investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework for preservation of the edges and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser mean of percent root mean square difference (PRD), mean square error (MSE), and better mean SNR improvement compared to other well-known methods at different input SNR. The proposed methodology also shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR.

14

Automatic sleep scoring using statistical features in the EMD domain and ensemble methods

Hassan A. R., Bhuiyan M. I. H.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

248--255

EN

An automatic sleep scoring method based on single channel electroencephalogram (EEG) is essential not only for alleviating the burden of the clinicians of analyzing a high volume of data but also for making a low-power wearable sleep monitoring system feasible. However, most of the existing works are either multichannel or multiple physiological signal based or yield poor algorithmic performance. In this study, we propound a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal. Decomposing the EEG signal segments using Empirical Mode Decomposition (EMD), we extract various statistical moment based features. The effectiveness of statistical features in the EMD domain is inspected. Statistical analysis is performed for feature selection. We then employ Adaptive Boosting and decision trees to perform classification. The performance of our feature extraction scheme is studied for various choices of classification models. Experimental outcomes manifest that the performance of the proposed sleep staging algorithm is better than that of the state-of-the-art ones. Furthermore, the proposed method's non-REM 1 stage detection accuracy is better than most of the existing works.

15

Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals

Djemili R., Bourouba H., Korba M. C. A.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

285--291

EN

Epilepsy is a neurological disorder affecting more than 50 million individuals in the world. Analysis of the electroencephalogram (EEG) is a powerful tool to assist neurologists for diagnosis and treatment. In this paper a new feature extraction method based on empirical mode decomposition (EMD) is proposed. The EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and four statistical parameters are calculated over these IMFs constituting the input feature vector to be fed to a multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the publicly available Bonn dataset show that an accurate classification rate of 100% is achieved in the discrimination between normal and ictal EEG, and an accuracy of 97.7% is reached in the classification of interictal and ictal EEG signals. Our results are equivalent or outperform recent studies published in the literature.

16

A new baroreflex sensitivity index based on improved Hilbert–Huang transform for assessment of baroreflex in supine and standing postures

Singh A., Saini B. S., Singh D.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 2

355--365

EN

The aim of this study is to propose a new baroreflex sensitivity (BRS) index using improved Hilbert–Huang transform (HHT) using weighted coherence (CW) criterion and apply it to assess baroreflex in supine and standing postures. Improved HHT is obtained by addressing the mode mixing and end effect problems associated with empirical mode decomposition which is a required step in the computation of HHT and thus mitigating the unwanted low frequency component from the power spectrum. This study was first performed on synthetic signals generated using integral pulse frequency model and further extended to real RR interval and systolic blood pressure records of 50 healthy subjects, 20 post acute myocardial infarction patients undergoing postural stress from supine to standing position. Evaluation is also performed on standard EuroBaVar database, comprising of 21 subjects, under supine and standing positions. The results are (i) enhanced values of supine-to-standing low frequency BRS index (α-LF) equal to 1.78 and high frequency BRS index (α-HF) equal to 2.48 are obtained using improved HHT compared to standard HHT (α-LF = 1.54, α-HF = 2.36) and traditional power spectral density (α-LF = 1.55, α-HF = 2.34) for healthy subjects, (ii) there is an increased rate of change of LF/HF power ratios from supine to standing positions, and (iii) number of BRS responses obtained using CW criterion are greater than those obtained by using mean coherence criterion. In conclusion, the new BRS index takes into consid-eration the non-linear nature of interactions between heart rate variability and systolic blood pressure variability.

17

Taking advantage of empirical mode decomposition in diagnosing gear faults

Łazarz B., Madej H., Czech P.

Diagnostyka

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2009

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nr 1(49)

67-72

EN

The study presents the application of empirical mode decomposition as a tool useful in diagnosing faults in gears. The method is a modern algorithm used for non-linear and non-stationary signals. Using this algorithm, it is possible to decompose a signal into a finite sum of component called intrinsic mode functions (IMF). For each IMF, the number of extremes and the number of transitions through zero is equal or different, by maximum one, and the mean value of envelope determined by the signal extremes equals zero. In practice, natural signals do not meet these conditions. In the experiment, a gearbox operating in a circulating power system was used, with 16 and 24 pinion and wheel teeth, respectively. The measurements were carried out for a non-damaged gear and for a gear with a modelled fault, operating at various rotational speeds and under different loads.

PL

W opracowaniu przedstawiono zastosowanie empirycznej dekompozycji sygnału jako narzędzia przydatnego w diagnostyce uszkodzeń przekładni zębatych. Metoda ta jest nowoczesnym algorytmem stosowanym dla sygnałów nieliniowych i niestacjonarnych. Wykorzystując ten algorytm można rozłożyć sygnał na skończoną sumę składowych zwanych funkcjami wewnętrznymi (IMF). Dla każdego IMF liczba ekstremów i liczba przejść przez zero jest równa bądź różna o maksimum jeden, a wartość średnia obwiedni określonej przez ekstrema sygnału równa się zero. W praktyce naturalne sygnały nie spełniają tych warunków. W eksperymencie wykorzystano przekładnie zębatą pracującą w układzie mocy krążącej o licznie zębów zębnika i koła odpowiednio 16 i 24. Pomiary przeprowadzono dla przekładni nieuszkodzonej oraz z zamodelowanym uszkodzeniem, pracującej przy różnych prędkościach obrotowych i różnych obciążeniach.