Journal articles: 'Non-stationary signal classification'

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Ratnayake, TA, DBW Nettasinghe, GMRI Godaliyadda, MPB Ekanayake, and JV Wijayakulasooriya. "An information rich subspace separation for non-stationary signal classification." Journal of the National Science Foundation of Sri Lanka 44, no. 3 (2016): 257. http://dx.doi.org/10.4038/jnsfsr.v44i3.8008.

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Świercz, Ewa. "Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 135–47. http://dx.doi.org/10.2478/v10006-010-0010-x.

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Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distributionA new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requiresa prioriknowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to themaxdiscriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

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Lowne, D. R., S. J. Roberts, and R. Garnett. "Sequential non-stationary dynamic classification with sparse feedback." Pattern Recognition 43, no. 3 (2010): 897–905. http://dx.doi.org/10.1016/j.patcog.2009.09.004.

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Shin, Younghak, Seungchan Lee, Minkyu Ahn, Hohyun Cho, Sung Chan Jun, and Heung-No Lee. "Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification." Biomedical Signal Processing and Control 21 (August 2015): 8–18. http://dx.doi.org/10.1016/j.bspc.2015.05.007.

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TACER, BERKANT, and PATRICK J. LOUGHLIN. "NON-STATIONARY SIGNAL CLASSIFICATION USING THE JOINT MOMENTS OF TIME-FREQUENCY DISTRIBUTIONS." Pattern Recognition 31, no. 11 (1998): 1635–41. http://dx.doi.org/10.1016/s0031-3203(98)00031-4.

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Ge, Mingtao, Jie Wang, Yicun Xu, Fangfang Zhang, Ke Bai, and Xiangyang Ren. "Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification." Symmetry 10, no. 12 (2018): 730. http://dx.doi.org/10.3390/sym10120730.

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Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symmetry of the rolling bearing is broken and the fault destroys the rolling bearing’s stable working state. Whenever the bearing passes through the fault point, it will send out vibration signals representing the fault characteristics. These signals are often non-linear, non-stationary, and full of Gaussian noise which are quite different from normal signals. According to this, the sub-modal obtained by empirical wavelet transform (EWT), secondary decomposition is tested by the Gaussian distribution hypothesis test. It is regarded that sub-modal following Gaussian distribution is Gaussian noise which is filtered during signal reconstruction. Then by taking advantage of the ambiguity function superiority in non-stationary signal processing and combining correlation coefficient, an ambiguity correlation classifier is constructed. After training, the classifier can recognize vibration signals of rolling bearings under different working conditions, so that the purpose of identifying rolling bearing faults can be achieved. Finally, the method effect was verified by experiments.

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Dong, Zeng Shou, Zhao Jing Ren, and You Dong. "Research of Mechanical Vibration Signal Classification Based on LMD." Applied Mechanics and Materials 764-765 (May 2015): 350–58. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.350.

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The traditional signal processing methods are difficult to accurately extract fault information, because mechanical fault vibration signals have non-stationary, which will cause system instability. Local mean decomposition is adaptive signal processing method. However, in the local mean decomposition of the signal, the trend of the endpoint can not be predicted which cause contaminating the entire signal sequence, the original moving average of the signal used over-smoothing treatment, resulting in fault characteristics can not accurately extract. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.

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Biswal, B., P. K. Dash, and S. Mishra. "Non-stationary power signal classification using local linear radial basis function neural networks." International Journal of Knowledge-based and Intelligent Engineering Systems 13, no. 2 (2009): 79–90. http://dx.doi.org/10.3233/jad-2009-0176.

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Jiang, Lingli, Yilun Liu, Xuejun Li, and Anhua Chen. "Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis." Shock and Vibration 18, no. 1-2 (2011): 127–37. http://dx.doi.org/10.1155/2011/703210.

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This paper proposes a new approach combining autoregressive (AR) model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

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Chatterjee, Shre Kumar, Saptarshi Das, Koushik Maharatna, et al. "Exploring strategies for classification of external stimuli using statistical features of the plant electrical response." Journal of The Royal Society Interface 12, no. 104 (2015): 20141225. http://dx.doi.org/10.1098/rsif.2014.1225.

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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli—sodium chloride (NaCl), sulfuric acid (H 2 SO 4 ) and ozone (O 3 ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

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Lweesy, K., N. Khasawneh, M. Fraiwan, H. Wenz, H. Dickhaus, and L. Fraiwan. "Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA." Methods of Information in Medicine 49, no. 03 (2010): 230–37. http://dx.doi.org/10.3414/me09-01-0054.

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Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

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Wang, Chao Jie, Hong Yi Li, Wei Xiang, and Di Zhao. "A New Signal Classification Method Based on EEMD and FCM and its Application in Bearing Fault Diagnosis." Applied Mechanics and Materials 602-605 (August 2014): 1803–6. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1803.

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In order to diagnose nonlinear and non-stationary fault signals in bearings, a new method is presented based on the ensemble empirical decomposition (EEMD) and the fuzzy c-means (FCM) clustering algorithm. At first, the bearing fault signals were decomposed using EEMD and the intrinsic mode functions (IMF) were produced. Second the energy ratios of these IMFs were computed and taken as the characteristic parameters for the FCM clustering algorithm. Then the FCM clustering method was conducted to classify the bearing fault signals into different classes. Finally, on the basis of the preceding classification results, we diagnosed a bearing fault through taking its distances between different cluster centers as the criteria. Experiments showed that the bearing fault signal classification results conformed to actualities well. The new signal classification method can be effectively utilized in bearing fault diagnosis.

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Kaplun, Dmitry, Alexander Voznesensky, Sergei Romanov, Valery Andreev, and Denis Butusov. "Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System." Applied Sciences 10, no. 9 (2020): 3097. http://dx.doi.org/10.3390/app10093097.

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This paper considers two approaches to hydroacoustic signal classification, taking the sounds made by whales as an example: a method based on harmonic wavelets and a technique involving deep learning neural networks. The study deals with the classification of hydroacoustic signals using coefficients of the harmonic wavelet transform (fast computation), short-time Fourier transform (spectrogram) and Fourier transform using a kNN-algorithm. Classification quality metrics (precision, recall and accuracy) are given for different signal-to-noise ratios. ROC curves were also obtained. The use of the deep neural network for classification of whales’ sounds is considered. The effectiveness of using harmonic wavelets for the classification of complex non-stationary signals is proved. A technique to reduce the feature space dimension using a ‘modulo N reduction’ method is proposed. A classification of 26 individual whales from the Whale FM Project dataset is presented. It is shown that the deep-learning-based approach provides the best result for the Whale FM Project dataset both for whale types and individuals.

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Khan, Nabeel A., and Sadiq Ali. "Classification of EEG Signals Using Adaptive Time-Frequency Distributions." Metrology and Measurement Systems 23, no. 2 (2016): 251–60. http://dx.doi.org/10.1515/mms-2016-0021.

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Abstract Time-Frequency (t-f) distributions are frequently employed for analysis of new-born EEG signals because of their non-stationary characteristics. Most of the existing time-frequency distributions fail to concentrate energy for a multicomponent signal having multiple directions of energy distribution in the t-f domain. In order to analyse such signals, we propose an Adaptive Directional Time-Frequency Distribution (ADTFD). The ADTFD outperforms other adaptive kernel and fixed kernel TFDs in terms of its ability to achieve high resolution for EEG seizure signals. It is also shown that the ADTFD can be used to define new time-frequency features that can lead to better classification of EEG signals, e.g. the use of the ADTFD leads to 97.5% total accuracy, which is by 2% more than the results achieved by the other methods.

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Sahu, G., B. Biswal, and A. Choubey. "Non-stationary signal classification via modified fuzzy C-means algorithm and improved bacterial foraging algorithm." International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 30, no. 2 (2016): e2181. http://dx.doi.org/10.1002/jnm.2181.

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Biswal, B., P. K. Dash, and B. K. Panigrahi. "Time Frequency Analysis and Non-Stationary Signal Classification using PSO Based Fuzzy C-Means Algorithm." IETE Journal of Research 53, no. 5 (2007): 441–50. http://dx.doi.org/10.1080/03772063.2007.10876159.

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SREE, S. VINITHA, DHANJOO N. GHISTA, and KWAN-HOONG NG. "CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 05 (2012): 1240032. http://dx.doi.org/10.1142/s0219519412400325.

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An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

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Xu, Fei, Guozheng Yan, Kai Zhao, Li Lu, Zhiwu Wang, and Jinyang Gao. "Quantifying the complexity of human colonic pressure signals using an entropy measure." Biomedical Engineering / Biomedizinische Technik 61, no. 1 (2016): 127–32. http://dx.doi.org/10.1515/bmt-2015-0026.

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Abstract Studying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification.

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PUTHANKATTIL, SUBHA D., and PAUL K. JOSEPH. "CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY." Journal of Mechanics in Medicine and Biology 12, no. 04 (2012): 1240019. http://dx.doi.org/10.1142/s0219519412400192.

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EEG is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing methods. This paper attempts to classify the EEG signals of normal and depression patients using well-established signal processing techniques involving relative wavelet energy (RWE) and artificial feedForward neural network. High frequency noise present in the recorded signal is removed using total variation filtering (TVF). Classification of the frequency bands of EEG signals into appropriate detail levels and approximation level is carried out using an eight-level multiresolution decomposition method of discrete wavelet transform (DWT). Parseval's theorem is used for calculating the energy at different resolution levels. RWE analysis gives information about the signal energy distribution at different decomposition levels. Both RWE and feedforward Network are used to classify the signals from normal controls and depression patients. The performance of the artificial neural network was evaluated using the classification accuracy and its value of 98.11% indicates a great potential for classifying normal and depression signals.

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Nguyen, Cong Dai, Alexander Prosvirin, and Jong-Myon Kim. "A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds." Sensors 20, no. 11 (2020): 3105. http://dx.doi.org/10.3390/s20113105.

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The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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KRISHNAN, M. MUTHU RAMA, S. VINITHA SREE, DHANJOO N. GHISTA, et al. "AUTOMATED DIAGNOSIS OF CARDIAC HEALTH USING RECURRENCE QUANTIFICATION ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 04 (2012): 1240014. http://dx.doi.org/10.1142/s0219519412400143.

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The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets.

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Chaudhary, Poonam, and Rashmi Agrawal. "Sensory motor imagery EEG classification based on non-dyadic wavelets using dynamic weighted majority ensemble classification." Intelligent Decision Technologies 15, no. 1 (2021): 33–43. http://dx.doi.org/10.3233/idt-200005.

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The classification accuracy has become a significant challenge and an important task in sensory motor imagery (SMI) electroencephalogram (EEG) based Brain Computer interface (BCI) system. This paper compares ensemble classification framework with individual classifiers. The main objective is to reduce the inference of non-stationary and transient information and improves the classification decision in BCI system. The framework comprises the three phases as follows: (1) the EEG signal first decomposes into triadic frequency bands: low pass band, band pass filter and high pass filter to localize α, β and high γ frequency bands within the EEG signals, (2) Then, Common spatial pattern (CSP) algorithm has been applied on the extracted frequencies in phase I to heave out the important features of EEG signal, (3) Further, an existing Dynamic Weighted Majiority (DWM) ensemble classification algorithm has been implemented using features extracted in phase II, for final class label decision. J48, Naive Bayes, Support Vector Machine, and K-Nearest Neighbor classifiers used as base classifiers for making a diverse ensemble of classifiers. A comparative study between individual classifiers and ensemble framework has been included in the paper. Experimental evaluation and assessment of the performance of the proposed model is done on the publically available datasets: BCI Competition IV dataset IIa and BCI Competition III dataset IVa. The ensemble based learning method gave the highest accuracy among all. The average sensitivity, specificity, and accuracy of 85.4%, 86.5%, and 85.6% were achieved with a kappa value of 0.59 using DWM classification.

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İçer, Semra, and Şerife Gengeç. "Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds." Digital Signal Processing 28 (May 2014): 18–27. http://dx.doi.org/10.1016/j.dsp.2014.02.001.

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Camci, Fatih, and Ratna Babu Chinnam. "General support vector representation machine for one-class classification of non-stationary classes." Pattern Recognition 41, no. 10 (2008): 3021–34. http://dx.doi.org/10.1016/j.patcog.2008.04.001.

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Kristomo, Domy, Risanuri Hidayat, and Indah Soesanti. "Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron." MATEC Web of Conferences 154 (2018): 03015. http://dx.doi.org/10.1051/matecconf/201815403015.

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The research on signal processing of syllables sound signal is still the challenging tasks, due to non-stationary, speaker-dependent, variable context, and dynamic nature factor of the signal. In the process of classification using multi-layer perceptron (MLP), the process of selecting a suitable parameter of hidden neuron and hidden layer is crucial for the optimal result of classification. This paper presents a speech signal classification method by using MLP with various numbers of hidden-layer and hidden-neuron for classifying the Indonesian Consonant-Vowel (CV) syllables signal. Five feature sets were generated by using Discrete Wavelet Transform (DWT), Renyi Entropy, Autoregressive Power Spectral Density (AR-PSD) and Statistical methods. Each syllable was segmented at a certain length to form a CV unit. The results show that the average recognition of WRPSDS with 1, 2, and 3 hidden layers were 74.17%, 69.17%, and 63.03%, respectively.

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Ma, Zhiyuan, Zhi Huang, Anni Lin, and Guangming Huang. "LPI Radar Waveform Recognition Based on Features from Multiple Images." Sensors 20, no. 2 (2020): 526. http://dx.doi.org/10.3390/s20020526.

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Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.

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Zhang, Xia, and Haijun Chen. "A SEIZURE DETECTION METHOD BASED ON WELL-SOLVED NONLINEAR AND NON-STATIONARY PROBLEMS WITH ELECTROENCEPHALOGRAPHIC SIGNALS." Biomedical Engineering: Applications, Basis and Communications 30, no. 05 (2018): 1850037. http://dx.doi.org/10.4015/s1016237218500370.

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The main focus of this paper is to solve the nonlinear and non-stationary problems in electroencephalographic (EEG) signals, which has been solved by the proposed method by using convolutional neural networks (CNN) as the classifiers and assembling Local Mean Decomposition (LMD) and cepstral coefficients as the feature extraction methods to achieve epileptic seizure detection with signal analysis and processing. In this proposed method, LMD and cepstral coefficients have been employed to solve the nonlinear and non-stationary problems in feature extraction and infusion, and then, the feature can be employed to feed to the recognition engine named CNN, and finally, the epileptic seizure detection can be achieved by this step. Publicly available EEG database from the University of Bonn (UoB), Germany had been used to verify the effectiveness and robustness of this proposed method on feature extraction. The complete dataset of total 7960 EEG segments, three recognition problems marked as AB versus CD versus E, the average classification accuracy of these segments can be generally obtained as highly as 99.84%, the maximal classification accuracy is 99.87%, and the lowest recognition accuracy is 98.74%. To the best of our knowledge, the excellent performance of the proposed method has shown that this method can be employed to track the patient’s healthy state and monitor the moment of epilepsy seizure.

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Trigui, Omar, Wassim Zouch, and Mohamed Ben Messaoud. "Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 3 (2017): 47–68. http://dx.doi.org/10.4018/ijcini.2017070104.

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The features extraction is the main step in a Brain-Computer Interface (BCI) design. Its goal is to create features easy to be interpreted in order to produce the most accurate control commands. For this end, these features must include all the original signal characteristics. The generated brain's signals' non-stationary and nonlinearity constitute a limitation to the improvement of the performances of systems based on traditional signal processing such as Fourier Transform. This work deals with the comparison of features extraction between Hilbert-Huang Transform (HHT) and Welch's method for Power Spectral Density estimation (PSD) then on the creation of an adaptive method combining the two. The parameters optimization of each method is firstly performed to reach the best classification accuracy rate. The study shows that the PSD estimation is sensitive to the parametric variation whereas the HHT method is mainly robust. The classification results show that an adaptive joint method can reach 90% of accuracy rate for a mental activity period of 1s.

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Khan, Nabeel Ali, and Sadiq Ali. "A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain." Computers in Biology and Medicine 100 (September 2018): 10–16. http://dx.doi.org/10.1016/j.compbiomed.2018.06.018.

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Hernández-Muriel, José Alberto, Andrés Marino Álvarez-Meza, Julián David Echeverry-Correa, Álvaro Ángel Orozco-Gutierrez, and Mauricio Alexánder Álvarez-López. "Feature relevance estimation for vibration-based condition monitoring of an internal combustion engine." TecnoLógicas 20, no. 39 (2017): 157–72. http://dx.doi.org/10.22430/22565337.698.

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Condition monitoring of Internal Combustion Engines (ICE) benefits cost-effective operations in the modern industrial sector. Because of this, vibration signals are commonly monitored as part of a non-invasive approach to ICE analysis. However, vibration-based ICE monitoring poses a challenge due to the properties of this kind of signals. They are highly dynamic and non-stationary, let alone the diverse sources involved in the combustion process. In this paper, we propose a feature relevance estimation strategy for vibration-based ICE analysis. Our approach is divided into three main stages: signal decomposition using an Ensemble Empirical Mode Decomposition algorithm, multi-domain parameter estimation from time and frequency representations, and a supervised feature selection based on the Relief-F technique. Accordingly, we decomposed the vibration signals by using self-adaptive analysis to represent nonlinear and non-stationary time series. Afterwards, time and frequency-based parameters were calculated to code complex and/or non-stationary dynamics. Subsequently, we computed a relevance vector index to measure the contribution of each multi-domain feature to the discrimination of different fuel blend estimation/diagnosis categories for ICE. In particular, we worked with an ICE dataset collected from fuel blends under normal and fault scenarios at different engine speeds to test our approach. Our classification results presented nearly 98% of accuracy after using a k-Nearest Neighbors machine. They reveal the way our approach identifies a relevant subset of features for ICE condition monitoring. One of the benefits is the reduced number of parameters.

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Achmamad, Abdelouahad, and Atman Jbari. "A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform." Bulletin of Electrical Engineering and Informatics 9, no. 4 (2020): 1420–29. http://dx.doi.org/10.11591/eei.v9i4.2381.

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Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.

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Achmamad, Abdelouahad, Abdelali Belkhou, and Atman Jbari. "Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (2020): 744. http://dx.doi.org/10.11591/ijai.v9.i4.pp744-756.

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Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.

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Valtierra-Rodriguez, Martin, Juan Amezquita-Sanchez, Arturo Garcia-Perez, and David Camarena-Martinez. "Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors." Mathematics 7, no. 9 (2019): 783. http://dx.doi.org/10.3390/math7090783.

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Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

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Virdi, Kulsheet Kaur, and Satish Pawar. "Hand and Leg Movement Prediction using EEG Signal by Stacked Deep Auto Encoder." SMART MOVES JOURNAL IJOSCIENCE 5, no. 10 (2019): 36–48. http://dx.doi.org/10.24113/ijoscience.v5i10.230.

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Brain Computer Interface (BCI) is device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. Brain–computer interfacing is an uprising field of research wherever signals extracted from the human brain are used for deciding and generation of control signals. Selection of the most appropriate classifier to find the mental states from electroencephalography (EEG) signal is an open research area due to the signal’s non-stationary and ergodic nature. In this research work the proposed algorithm is designed to solve an important application in BCI where left hand forward–backward movements and right hand forward-backward movements as well as left leg movement and right leg movement are needed to be classified. Features are extracted from these datasets to classify the type of movements. A staked Deepauto encoder is used for classification of hand and leg movements and compared with other classifiers. The accuracy of stacked deepauto encoder is better with respect to other classifiers in terms of classification of hand and leg movement of EEG signals.

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Li, DZ, X. Zheng, QW Xie, and QB Jin. "A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 232, no. 6 (2017): 635–49. http://dx.doi.org/10.1177/0954408917733130.

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A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.

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Liu, Yang, Lixiang Duan, Zhuang Yuan, Ning Wang, and Jianping Zhao. "An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE." Sensors 19, no. 5 (2019): 1041. http://dx.doi.org/10.3390/s19051041.

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The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

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Tao, Ran, You Cai Xu, Xin Shi Li, Shu Guo, Kun Li, and Min Gou. "The Research of Fault Diagnosis Method of Roller Bearing Based on EMD and VPMCD." Advanced Materials Research 1014 (July 2014): 505–9. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.505.

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Empirical mode decomposition (EMD) can extract real time-frequency characteristics from the non-stationary and nonlinear signal. Variable prediction model based class discriminate (VPMCD) is introduced into roller bearing fault diagnosis in this paper. Therefore, a fault diagnosis method based on EMD and VPMCD is put forward in the paper. Firstly, the different feature vectors in the signal are extracted by EMD. Then, different fault models of roller bearing are distinguished by using VPMCD. Finally, an simulation example based on EMD and VPMCD is shown in this paper. The results show that this method can gain very stable classification performance and good computational efficiency.

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Bhuiyan, Md Jashim Uddin, and Mollah Rezaul Alam. "Classification of Power Quality Disturbances using Mahalanobis Distance Classifier and Stockwell Transformation." AIUB Journal of Science and Engineering (AJSE) 17, no. 1 (2018): 19–24. http://dx.doi.org/10.53799/ajse.v17i1.4.

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Detection and classification of PQ (Power Quality) disturbances in distribution/transmission systems are very important for protection of electricity network. Most of the disturbances of power network are non-stationary and momentary in nature, hence it requires advanced tools and techniques for the analysis and classification of PQ disturbances. This paper presents the detection and classification of PQ events or disturbances employing Stockwell-Transformation, known as S-Transformation, and Mahalanobis Distance (MD) based approach. The proposed method exploits only four features extracted through S-transformation of the voltage signal; then, using these four features, classification is conducted by MD based classifier. In this work, classification of several PQ disturbances, such as, voltage sags, swells, harmonics, notch, flicker, transient oscillation, momentary interruption, etc., are considered. The simulation results demonstrate that the proposed method is very effective and accurate in detecting and classifying PQ events. Validation of the proposed approach has also been conducted using real signal recorded in IEEE 1159.2 database. Moreover, comparative classification performance of MD based classifierwith MED (minimum Euclidean distance) and LVQ (learning vector quantization) reveals the superiority of the proposed approach.

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Shafiqa Wan Musa, Wan Siti Nur, Mohd Ibrahim Shapiai, Hilman Fauzi, and Aznida Firzah Abdul Aziz. "Vascular dementia classification based on hilbert huang transform as feature extractor." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (2020): 968. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp968-974.

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<a name="_Hlk14121907"></a>Impairment of cognitive and working memory after stroke was common. Vascular dementia (VaD) was a prevalent type of dementia that was caused by an impaired blood supply to the brain because of a series of small strokes. Electroencephalogram (EEG) gives information about brain status and activity, so it had a lot of potential to be used in diagnosing people with dementia. Since the EEG signal is extremely non-linear and non-stationary data, traditional Fourier analysis such as Fast Fourier Transform (FFT) that broadens sinusoidal signals cannot describe the amplitude contribution of each frequency value in specific time. Meanwhile, Hilbert Huang Transform (HHT) was based on the characteristic local time scale of the signal, it can efficiently obtain instantaneous frequency and instantaneous amplitude for nonstationary and nonlinear data. In this paper, HHT was employed as feature extraction method to extract the energy features of frequency bands from post stroke patients and healthy subjects. The extracted features were fed into extreme learning machine (ELM) for classifying post stroke patient with VaD and healthy subjects. The results of classification accuracy using HHT as feature extractor and FFT as feature extractor were compared. The mean accuracy of classification using HHT was 59.14%, respectively, while mean accuracy of classification using FFT was 94.4%, respectively, in classifying post stroke patient with VaD and healthy subjects.

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Chen, Zhongye, Yijun Wang, and Zhongyan Song. "Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method." Sensors 21, no. 14 (2021): 4646. http://dx.doi.org/10.3390/s21144646.

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In recent years, more and more frameworks have been applied to brain-computer interface technology, and electroencephalogram-based motor imagery (MI-EEG) is developing rapidly. However, it is still a challenge to improve the accuracy of MI-EEG classification. A deep learning framework termed IS-CBAM-convolutional neural network (CNN) is proposed to address the non-stationary nature, the temporal localization of excitation occurrence, and the frequency band distribution characteristics of the MI-EEG signal in this paper. First, according to the logically symmetrical relationship between the C3 and C4 channels, the result of the time-frequency image subtraction (IS) for the MI-EEG signal is used as the input of the classifier. It both reduces the redundancy and increases the feature differences of the input data. Second, the attention module is added to the classifier. A convolutional neural network is built as the base classifier, and information on the temporal location and frequency distribution of MI-EEG signal occurrences are adaptively extracted by introducing the Convolutional Block Attention Module (CBAM). This approach reduces irrelevant noise interference while increasing the robustness of the pattern. The performance of the framework was evaluated on BCI competition IV dataset 2b, where the mean accuracy reached 79.6%, and the average kappa value reached 0.592. The experimental results validate the feasibility of the framework and show the performance improvement of MI-EEG signal classification.

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Piltan, Farzin, and Jong-Myon Kim. "Hybrid Fault Diagnosis of Bearings: Adaptive Fuzzy Orthonormal-ARX Robust Feedback Observer." Applied Sciences 10, no. 10 (2020): 3587. http://dx.doi.org/10.3390/app10103587.

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Rolling-element bearings (REBs) make up a class of non-linear rotating machines that can be applied in several activities. Conceding a bearing has complicated and uncertain behavior that exhibits substantial challenges to fault diagnosis. Thus, the offered anomaly-diagnosis algorithm, based on a fuzzy orthonormal-ARX adaptive fuzzy logic-structure feedback observer, is developed. A fuzzy orthonormal-ARX algorithm is presented for non-stationary signal modeling. Next, a robust, stable, reliable, and accurate observer is developed for signal estimation. Therefore, firstly, a classical feedback observer is implemented. To address the robustness drawback found in feedback observers, a multi-structure technique is developed. Furthermore, to generate signal estimation performance and reliability, the fuzzy logic technique is applied to the structure feedback observer. Also, to improve the stability, reliability, and robustness of the fuzzy orthonormal-ARX fuzzy logic-structure feedback observer, an adaptive algorithm is developed. After generating the residual signals, a support vector machine (SVM) is developed for the detection and classification of the bearing fault conditions. The effectiveness of the proposed procedure is validated using two different datasets for single-type fault diagnosis based on the Case Western Reverse University (CWRU) vibration dataset and multi-type fault diagnosis of bearing using the Smart Health Safety Environment (SHSE) Lab acoustic emission dataset. The proposed algorithm increases the classification accuracy from 86% in the SVM-based fuzzy orthonormal-ARX feedback observer to 97.5% in single-type fault and from 80% to 98.3% in the multi-type faults.

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Uyulan, Caglar, Türker Tekin Ergüzel, and Nevzat Tarhan. "Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification." Biomedical Engineering / Biomedizinische Technik 64, no. 5 (2019): 529–42. http://dx.doi.org/10.1515/bmt-2018-0105.

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Abstract Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain.

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Xiao, Shungen, Ang Nie, Zexiong Zhang, Shulin Liu, Mengmeng Song, and Hongli Zhang. "Fault Diagnosis of a Reciprocating Compressor Air Valve Based on Deep Learning." Applied Sciences 10, no. 18 (2020): 6596. http://dx.doi.org/10.3390/app10186596.

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With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy.

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Zwanenberg, Oliver van, Sophie Triantaphillidou, Robin Jenkin, and Alexandra Psarrou. "Camera System Performance Derived from Natural Scenes." Electronic Imaging 2020, no. 9 (2020): 241–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.9.iqsp-241.

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The Modulation Transfer Function (MTF) is a wellestablished measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Nonlinearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce ‘live’ performance measures, acquired directly from camera feeds.

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Wang, Xiaochao, Zhenggang Lu, Juyao Wei, and Yuan Zhang. "Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy." Entropy 21, no. 9 (2019): 865. http://dx.doi.org/10.3390/e21090865.

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The fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosis based on frequency-domain energy feature reconstruction (EFR) and composite multiscale permutation entropy (CMPE). First, a wavelet packet transform (WPT) is applied to decompose the vibration signals into multiple frequency bands. Then, considering that the bearing-localized defects cause the axle-box bearing system to resonate at a high frequency, which will lead to uneven energy distribution of the signal in the frequency domain, the energy factors of each frequency band are calculated by an energy feature extraction algorithm, from which the frequency band with maximum energy factor (which contains abundant fault information) is reconstructed to the time-domain signal. Next, the complexity of the reconstructed signals is calculated by CMPE as fault feature vectors. Finally, the feature vectors are input into a medium Gaussian support vector machine (MG-SVM) for bearing condition classification. The proposed method is validated by a public bearing data set and a wheelset-bearing system test bench data set. The experimental results indicate that the proposed method can effectively extract bearing fault features and provides a new solution for condition monitoring and fault diagnosis of rail vehicle axle-box bearings.

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Wang, Xiang, and Yuan Zheng. "Vibration Fault Diagnosis for Wind Turbine Based on Enhanced Supervised Locally Linear Embedding." Advanced Materials Research 1008-1009 (August 2014): 983–87. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.983.

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Fault diagnosis for wind turbine is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine. Fault diagnosis is essentially a kind of pattern recognition. In this paper, a novel fault diagnosis method based on enhanced supervised locally linear embedding is proposed for wind turbine. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Enhanced supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The wind turbine gearbox ball bearing vibration fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

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Wang, Xiao Yun. "Fault Diagnosis on Transmission System of Wind Turbines Based on Wavelet Packet Transform and RBF Neural Networks." Applied Mechanics and Materials 373-375 (August 2013): 1102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1102.

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Wind turbine transmission system with abundant fault feature and variable types, the vibration signal was a carrier of fault features and it can reflect most of the fault information in the wind turbine transmission system. As there were a large number of transient and non-stationary signals accompany with the vibration signals, so wavelet packet transform was adopted for feature extraction. As RBF Neural network has a strong nonlinear mapping ability and self-adaptability, so it was introduced to the diagnosis system for network training, the neural networks structure and learning algorithm was presented, which could enhance the accuracy of diagnosis. The two-level neural networks recognition method was proposed, first level for fault classification and second level for fault diagnosis. The example shows that this method can be effectively applied to transmission system of wind turbine fault diagnosis with wavelet packet algorithm for fault feature extraction and RBF neural network for pattern recognition.

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Hernández-Muriel, José Alberto, Jhon Bryan Bermeo-Ulloa, Mauricio Holguin-Londoño, Andrés Marino Álvarez-Meza, and Álvaro Angel Orozco-Gutiérrez. "Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM." Applied Sciences 10, no. 15 (2020): 5170. http://dx.doi.org/10.3390/app10155170.

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Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.

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Xu, You Cai, Xin Shi Li, Ran Tao, Shu Guo, Min Gou, and Kun Li. "The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis." Advanced Materials Research 1014 (July 2014): 510–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.510.

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The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

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Minh Khoa, Ngo, and Le Van Dai. "Detection and Classification of Power Quality Disturbances in Power System Using Modified-Combination between the Stockwell Transform and Decision Tree Methods." Energies 13, no. 14 (2020): 3623. http://dx.doi.org/10.3390/en13143623.

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The detection, mitigation, and classification of power quality (PQ) disturbances have been issues of interest in the power system field. This paper proposes an approach to detect and classify various types of PQ disturbances based on the Stockwell transform (ST) and decision tree (DT) methods. At first, the ST is developed based on the moving, localizing, and scalable Gaussian window to detect five statistical features of PQ disturbances such as the high frequency of oscillatory transient, distinction between stationary and non-stationary, the voltage amplitude oscillation around an average value, the existence of harmonics in a disturbance signal, and the root mean square voltage at the internal period of sag, swell or interruption. Then, these features are classified into nine types, such as normal, sag, swell, interruption, harmonic, flicker, oscillatory transient, harmonic voltage sag, and harmonic voltage swell by using the DT algorithm that is based on a set of rules with the structure “if…then’’. This proposed study is simulated using MATLAB simulation. The IEEE 13-bus system, the recorded real data based on PQube, and the experiment based on the laboratory environment are applied to verify the effectiveness.

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Journal articles on the topic 'Non-stationary signal classification'

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