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1

Application of low-cost ADXL1002 accelerometer for vehicle engine misfire detection using a novel hybrid EMD-based image processing and DCNN-LSTM model

Ahsan Muhammad, Bismor Dariusz, Fabiś Paweł

Vibrations in Physical Systems

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2025

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Vol. 36, nr 1

art. no. 2025102

EN

Engine misfires significantly impact vehicle performance and efficiency. This paper presents a novel misfire detection method using vibration data from an ADXL1002 accelerometer. The proposed approach employs Empirical Mode Decomposition (EMD) to effectively extract relevant frequency components from vibration signals, enhancing feature separation. The processed data is then transformed into 2D grayscale images and fed into a hybrid Deep Convolutional Neural Network-Long Short-Term Memory (DCNN-LSTM) model for classification. Experimental results showcase outstanding performances, achieving 100% training accuracy and 98.7% test accuracy. Comprehensive evaluation metrics-including sensitivity, specificity, balanced accuracy (BA), and geometric mean (GM)-validate the model’s robustness in detecting misfires across diverse operating conditions.

2

The statistical-based diagnosis with usage of acoustic sound decomposition and projected LSTM network of induction motors

Zastępa Marek

Eksploatacja i Niezawodność

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2025

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Vol. 27, no. 3

art. no. 205651

EN

The use of acoustic signals in the diagnosis of electrical machines allows for non-invasive and rapid diagnostics. The author proposed the novel approach of acoustic diagnosis of single-phase induction motors, which is 98.67% accurate on the test set and allows for fault detection in circa 0.042 s, and 97.33% accurate for 0.021 s long samples similarly. The research includes five classes of faults. In this method, intrinsic mode functions (IMFs) gained from the empirical mode decomposition (EMD) of the motor sound are used to calculate the following statistical parameters: mean, mean square, root mean square, standard deviation, energy, and norm. Next, these parameters are organized from a prepared matrix to a vector of parameters one IMF by one, suitable for neural network input. Such prepared data is then passed to the proposed architecture of the projected LSTM neural network. The training processes were fast - they took only 12 and 13 seconds selectively. The presented novel method is useful for acoustic fault diagnosis of electric motors and could be used for other motors.

3

Electroencephalograph (EEG) based classification of mental arithmetic using explainable machine learning

Aslam Murtaza, Rajbdad Fozia, Azmat Shoaib, Perveen Kausar, Naraghi-Pour Morteza, Xu Jian

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 2

154--169

EN

Mental arithmetic can be helpful for the evaluation of neurodevelopmental disorders arising from atypical development of the brain. We propose a novel explainable machine learning method for classifying mental arithmetic calculation tasks from resting brain states and good from bad calculations using Electroencephalography. Empirical mode decomposition features are extracted from intrinsic mode functions of the average signals of all trials. Most relevant features to the mental arithmetic tasks are ranked by a random forest-based recursive feature elimination method. These features identify the changes in frequency bands of the brain rhythms, such as delta, theta, and alpha, during mental tasks for the first time in literature. These unique explainable features are also used to identify brain areas such as frontal, temporal, and occipital lobes involved in mental arithmetic tasks. Moreover, our approach describes the memory regions and that bad calculations excite the brain areas, mostly related to emotions such as frustration and anxiety due to stressful mental arithmetic. Using a random forest classifier, beating the state-of-the-art, this method achieved classification accuracies of 99.30 % and 98.33 % for resting vs calculation and good vs bad calculation brain tasks, respectively. Also, our method outperformed the state of art in handling the inter-subject variability and achieved 98.17 ± 0.47 % and 97.19 ± 0.95 % classification accuracies for resting vs calculation and good vs bad calculation tasks, respectively.

4

EMD-based time-frequency analysis methods of audio signals

Lewandowski Marcin, Deng Qizhang

International Journal of Electronics and Telecommunications

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2024

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Vol. 70, No. 2

323--329

EN

Using appropriate signal processing tools to analyze time series data accurately is essential for correctly interpreting the underlying processes. Commonly employed methods include kernel-based transforms that utilize base functions and modifications to depict time series data. This paper refers to the analysis of audio data using two such transforms: the Fourier transform and the wavelet transform, both based on assumptions regarding the signal's linearity and stationarity. However, in audio engineering, these assumptions often do not hold as the statistical characteristics of most audio signals vary over time, making them unsuitable for treatment as outputs from a Linear Time-Invariant (LTI) system. Consequently, more recent methods have shifted towards breaking down signals into various modes in an adaptive, data-specific manner, potentially offering benefits over traditional kernel-based methods. Techniques like empirical mode decomposition and Holo-Hilbert Spectral Analysis are examples of this. The effectiveness of these methods was tested through simulations using speech signals for both kernel-based and adaptive decomposition methods, demonstrating that these adaptive methods are effective for analyzing audio data that is both nonstationary and an output of the nonlinear system.

5

Analysis of the Vehicle Engine Misfires using Frequency-Domain Approaches at Various RPMs with ADXL1002 Accelerometer

Ahsan Muhammad, Bismor Dariusz, Fabiś Paweł

Archives of Acoustics

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2024

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Vol. 49, nr 4

507--516

EN

Vehicle engine vibration signals acquired using MEMS sensors are crucial in the diagnosis of engine malfunctions, notably misfires due to unwanted signals and external noises in the recorded vibration dataset. In this study, the ADXL1002 accelerometer interfaced with the Beaglebone Black microcontroller is employed to capture vibration signals emitted by the vehicle engine across various operational states, including unloaded, loaded, and misfire conditions at 1500 RPMs, 2500 RPMs, and 3000 RPMs. In conjunction with the acquisition of this raw vibration data, frequency-domain signal processing techniques are employed to meticulously analyze and diagnose the distinct signatures of misfire occurrences across various engine speeds and loads. These techniques encompass the fast Fourier transform (FFT), envelope spectrum (ES), and empirical mode decomposition (EMD), each tailored to discern and characterize the nuanced vibration patterns associated with misfire events at different operational conditions.

6

Estimation of monthly evaporation values using gradient boosting machines and mode decomposition techniques in the Southeast Anatolia Project (GAP) area in Turkey

Sarigöl Metin, Katipoglu Okan Mert

Acta Geophysica

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2024

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Vol. 72, no. 2

999--1016

EN

Today, the biggest issue appears to be the increase in drought in some regions brought on by global warming, which has greatly increased the significance of water management. In light of evaporation's effect on drought, this research intends to evaluate the effectiveness of hybrid machine learning (ML) models, such as the Gradient Boosting Machines (GBM) technique paired with Empirical Mode Decomposition (EMD), Robust Empirical Mode Decomposition (REMD), Ensemble Empirical Mode Decomposition (EEMD), and Variational Mode Decomposition (VMD) signal decomposition techniques, for monthly evaporation prediction models in the Southeast Anatolia Project Area. In the design of the models, 80% of the data was used for training and 20% for testing. Furthermore, tenfold cross-validation was applied to solve the overfitting problem, which negatively affected the forecast performance. In the model setup, various combinations of precipitation, average air temperature, minimum air temperature, maximum air temperature, wind speed, actual air pressure, relative humidity, and solar time variables are presented to artificial intelligence models as input. The study revealed that the GBM methodology in combination with the signal decomposition methods REMD, EMD, EEMD, and VMD generally allowed for more accurate evaporation estimations than the GBM model alone. The study’s results are essential in relation to agricultural production, irrigation planning, water resources management studies, and hydrological modeling studies in the region.

7

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

|

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.

8

A hybrid denoising method for gyroscopes based on multiple screening

Rong Hailong, Jin Tianlei, Wang Hao, Wu Xiaohui, Zou Ling

Metrology and Measurement Systems

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2023

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Vol. 30, nr 4

755--772

EN

To reduce the random error of microelectromechanical system (MEMS) gyroscope, a hybrid method combining improved empirical mode decomposition (EMD) and least squares algorithm (LS) is proposed. Firstly, based on the multiple screening mechanism, intrinsic mode functions (IMFs) from the first decomposition are divided into noise IMFs, strong noise mixed IMFs, weak noise mixed IMFs and signal IMFs. Secondly, according to their characteristics, they are processed again. IMFs from the second decomposition are divided into noise IMFs and signal IMFs. Finally, useful signal is gathered to obtain the final denoising signal. Compared with some other denoising methods proposed in recent years, the experimental results show that the proposed method has obvious advantages in suppressing random error, greatly improving the signal quality and improving the accuracy of inertial navigation.

9

Two-dimensional EMD with shape-preserving spline interpolation

Brown Wesley G., Walt Maria D. van der

Journal of Applied Analysis

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2023

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Vol. 29, nr 2

287--296

EN

Empirical mode decomposition (EMD) is a popular, user-friendly, data-driven algorithm to decompose a given (non-stationary) signal into its constituting components, utilizing spline interpolation. This algorithm was first proposed in 1998 in the one-dimensional setting, and it employed standard cubic spline interpolation. Since then, different two-dimensional extensions of EMD have been proposed. In this paper, we consider one of these two-dimensional extensions and adapt it to use a shape-preserving interpolation scheme based on quadratic B-splines, ensuring that monotonicity and concavity in the input data are preserved. Using multiple numerical experiments, we show that this new scheme outperforms the original EMD, both qualitatively and quantitatively.

10

An Improved EMD Method Based on Utilizing Certain Inflection Points in the Construction of Envelope Curves

Kafil Mohsen, Darabi Kaveh, Ziaei-Rad Saeed

Archives of Acoustics

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2023

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Vol. 48, No. 3

389--401

EN

The empirical mode decomposition (EMD) algorithm is widely used as an adaptive time-frequency analysis method to decompose nonlinear and non-stationary signals into sets of intrinsic mode functions (IMFs). In the traditional EMD, the lower and upper envelopes should interpolate the minimum and maximum points of the signal, respectively. In this paper, an improved EMD method is proposed based on the new interpolation points, which are special inflection points (SIPn) of the signal. These points are identified in the signal and its first (n − 1) derivatives and are considered as auxiliary interpolation points in addition to the extrema. Therefore, the upper and lower envelopes should not only pass through the extrema but also these SIPn sets of points. By adding each set of SIPi (i = 1, 2, n) to the interpolation points, the frequency resolution of EMD is improved to a certain extent. The effectiveness of the proposed SIPn-EMD is validated by the decomposition of synthetic and experimental bearing vibration signals.

11

EMD-based time-frequency analysis methods of non-stationary audio signals

Lewandowski Marcin, Grodzicka Salomea

Vibrations in Physical Systems

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2022

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Vol. 33, nr 2

art. no. 2022215

EN

To ensure that any time series data is appropriately interpreted, it should be analyzed with proper signal processing tools. The most common analysis methods are kernel-based transforms, which use base functions and their modifications to represent time series data. This work discusses an analysis of audio data and two of those transforms - the Fourier transform and the wavelet transform based on a priori assumptions about the signal's linearity and stationarity. In audio engineering, these assumptions are invalid because the statistical parameters of most audio signals change with time and cannot be treated as an output of the LTI system. That is why recent approaches involve decomposition of a signal into different modes in a data-dependent and adaptive way, which may provide advantages over kernel-based transforms. Examples of such methods include empirical mode decomposition (EMD), ensemble EMD (EEMD), variational mode decomposition (VMD), or singular spectrum analysis (SSA). Simulations were performed with speech signal for kernel-based and data-dependent decomposition methods, which revealed that evaluated decomposition methods are promising approaches to analyzing non-stationary audio data.

12

Results in Q-measure

Jisha C. R.

Mathematica Applicanda

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2022

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Vol. 50, no. 2

183--215

EN

This paper introduces the notion of a generalized measure for a sequence of functions with oscillation and concentration effects. This measure is constructed by averaging the sequence of Borel measurable functions using singular or regular perturbations. In this way, the generalized limits of such sequences are conceptualized by enlarging the space of functions to measure spaces. It is a modification of the Young measure. This modified measure was termed a Q-measure. It can be difficult to determine the Young measure for a broad function. The Q-measure can be easily calculated for particular functions. This is one of the advantages of this study. As an application of the measure, we can define another weaker type of Monotone convergence theorem, the Lebesgue-dominated convergent theorem. A notion of average for underlying sequences to define the Q-measure is given, as also its application in signal analysis and atmospheric sciences.

PL

W tym artykule autor wprowadza nową miarę, którą nazywa miarą Q, reprezentującą słabą∗ granicę barycentrum ciągu funkcji borelowskich. Omawia niektóre wyniki zwi¡zane z tą miarą, co jest pomocne przy wyznaczaniu miary Q dla poszczególnych typów funkcji. Ponadto omówiono zastosowanie koncepcji średniej w analizie sygnałów i naukach o atmosferze.

13

Q-functional applications

Jisha C. R.

Mathematica Applicanda

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2022

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Vol. 50, no. 2

217--233

EN

The Q-measure indicate a weak∗ limit of the barycenter of a sequence of Borel measurable functions. In this paper, we will look only at Q-functional. Q-functional is defined by Q- measure, it is useful in the field of optimization. Computational results for Q-functional are presented and compared with Young functional. The obtained analytical results demonstrate relative error in Q-functional is lesser compared to Young functional.

PL

Miara Q wyznacza słabą∗ granicę barycentrum ciągu funkcji borelowskich. W tym artykule przyjrzymy się tylko funkcjonałom Q. Q-funkcjonalność jest definiowana przez miarę Q i jest przydatna w zastosowaniu do zadań optymalizacji. Przedstawiono wyniki obliczeń dla funkcjonału Q i porównano je z funkcjonałem Younga. Otrzymane wyniki analityczne pokazują, że błąd względny w funkcjonale Q jest mniejszy w porównaniu z funkcjonałem Younga.

14

Graphical representation and variability quantification of handwriting signals: New tools for Parkinson’s disease detection

Dehghanpur Deharab Elham, Ghaderyan Peyvand

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

158--172

EN

A reliable computer-aided method for Parkinson’s disease (PD) detection can slow down its progression and improve the life quality of patients. In this study, a new non-invasive and cost-effective method based on the online analysis of handwriting signals has been proposed. First, the dynamic handwriting signals have been converted into two graphical representations of the variability rate. Then, two new feasible features, including area of the analytic signal representation and area of the second-order difference plot, have been used to quantify the variability rate of handwriting signals. A statistical test and support vector machine classifier have been applied in a comparative study to test the impact of each variability feature, writing task, and time sequence on the detection performance, separately. The obtained results on PaHaW database with 35 Parkinson’s disease patients and 36 healthy controls have shown that the proposed method of handwriting variability feature extraction has effective performance and the capability for the PD detection. It has achieved an average sensitivity of 86.26% with only two types of features, providing a trade-off between the performance, the computational complexity, and interpretability of the motor patterns from the point of view of clinicians and neuropsychologists. Xcoordinate time-series and writing a sentence can achieve superior accuracy and robustness in the presence of individual differences. The experimental results have demonstrated that extracting the variability features that used graphical representations of the global changes in oscillatory mode has the ability to clinically describe the pathological dynamics of the handwriting signals for the PD identification.

15

EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning

Salankar Nilima, Qaisar Saeed Mian, Pławiak Paweł, Tadeusiewicz Ryszard, Hammad Mohamed

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

173--186

EN

The excessive drinking of alcohol can disrupt the neural system. This can be observed by properly analysing the Electroencephalogram (EEG) signals. However, the EEG is a signal of complex nature. Therefore, an accurate categorization between alcoholic (A) and nonalcoholic (NA) subjects, while using a short time EEG recording, is a challenging task. In this paper a novel hybridization of the oscillatory modes decomposition, features mining based on the Second Order Difference Plots (SODPs) of oscillatory modes, and machine learning algorithms is devised for an effective identification of alcoholism. The Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used to respectively decompose the considered EEG signals in Intrinsic Mode Functions (IMFs) and Modes. Onward, the SODPs, derived from first six IMFs and Modes, are considered. Features of SODPs are mined. To reduce the dimension of features set and computational complexity of the classification model, the pertinent features selection is made on the basis of Wilcoxon statistical test. Three features with p-values (p) of < 0.05 are selected from each intended SODP and these are the Central Tendency Measure (CTM), area and mean. These features are used for the discrimination between A and NA classes. In order to determine a suitable EEG signal segment length for the intended application, experiments are performed by considering features extracted from three different length time windows. The classification is carried out by using the Least Square Support Vector Machine (LS-SVM), Multilayer perceptron neural network (MLPNN), K-Nearest Neighbour (KNN) and Random Forest (RF) algorithms. The applicability is tested by using the UCI-KDD EEG dataset. The results are noteworthy for MLPNN with 99.89% and 99.45% accuracies for EMD and VMD respectively for 8-second window.

16

Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure

Lu Weijia, Dong Jiafan, Pan Yuheng, Li Guoya, Guo Jinpeng

Archives of Civil Engineering

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2022

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Vol. 68, nr 4

653--667

EN

Time series models have been used to extract damage features in the measured structural response. In order to better extract the sensitive features in the signal and detect structural damage, this paper proposes a damage identification method that combines empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) models. EMD decomposes nonlinear and non-stationary signals into different intrinsic mode functions (IMFs) according to frequency. IMF reduces the complexity of the signal and makes it easier to extract damage-sensitive features (DSF). The ARIMA model is used to extract damage sensitive features in IMF signals. The damage sensitive characteristic value of each node is used to analyze the location and damage degree of the damaged structure of the bridge. Considering that there are usually multiple failures in the actual engineering structure, this paper focuses on analysing the location and damage degree of multi-damaged bridge structures. A 6-meter-long multi-destructive steel-whole vibration experiment proved the state of the method. Meanwhile, the other two damage identification methods are compared. The results demonstrate that the DSF can effectively identify the damage location of the structure, and the accuracy rate has increased by 22.98% and 18.4% on average respectively.

17

Comparison of multiband filtering, empirical mode decomposition and short-time fourier transform used to extract physiological components from long-term heart rate variability

Adamczyk Krzysztof, Polak Adam G.

Metrology and Measurement Systems

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2021

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Vol. 28, nr 4

643--660

EN

Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.

18

Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

Zheng Guoxiao, Sun Weifang, Zhang Hao, Zhou Yuqing, Gao Chen

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 4

612--618

EN

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.

19

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.

20

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

|

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.