Kurtosis Index Research Articles

Fault Diagnosis of Rolling Element Bearings Based on Adaptive Mode Extraction

Generally speaking, vibration signals collected by sensors always contain complex frequency components, which will bring great trouble to bearing condition monitoring and fault diagnosis. A reliable fault signal component extraction method is significant to detect the fault-induced weak repetitive transients. Therefore, many signal decomposition or extraction methods have been developed and are widely employed in fault diagnosis. Based on the recently proposed variational mode extraction (VME) method, an adaptive optimal mode extraction method was designed with a new strategy to extract the mode center frequency and a novel indicator to optimize the balance parameter. The spectrum is first divided into several modes by enveloping curve fitting (ECF), and the center frequencies of each mode are extracted, respectively. All potential fault modes are then extracted sequentially utilizing the extracted center frequency and fixed balance parameter. For the extracted modes, the kurtosis index is applied to select the target mode. Finally, the relative amplitude ratio (RAR) index is used to adaptively adjust the balance parameter. The comparison results reveal that the adaptive mode extraction method can extract the weak fault feature under strong interference.

Fault Feature Extraction of Automobile Wheel Hub Bearing Based on RSK and RSSD

Abstract Wheel hub bearing not only bears axial load but also radial load, and then its running status directly affects the performance and safety of the automobile. In particular, complicated and variable driving conditions of the automobile are not only liable to aggravate the occurrence of mechanical fault such as localized wear, but also often cause powerful intermittent interference noise. It is difficult to extract the vibration characteristics of the transient impact and harmonic components of the wheel bearings in the event of a local fault. An extraction method of fault feature based on ration of smooth and kurtosis (RSK) index and resonance-based signal sparse de-composition (RSSD) is proposed for automobile wheel hub bearings, which aims at the minimum value of RSK index, sequentially optimizes the Q-factor of the resonance sparse decomposition, and obtains its optimal value adaptively to achieve the decomposition of low resonance components with transient impact components and high resonance components with harmonic components. The effect of the method has been verified by powerful intermittent interference simulation signals and experiment signals from local fault of wheel hub bearing with periodic dynamic radial loading.

Diagnosing utility grid disturbances in photovoltaic integrated DC microgrid using adaptive multiscale morphology with DFA analysis

In this study, a novel approach is proposed to distinguish both single and mixed power quality (PQ) disturbances in the network connected to the utility grid in a photovoltaic (PV) integrated DC microgrid. To analyze PQ disturbances in the proposed system, AC voltage signals are captured at the PCC of the utility grid. The captured voltage signals contain strong noise which impacts the system robustness. To resolve this problem, an adaptive multiscale improved combination morphological filter (AMICMF) technique is introduced in the proposed system. In this technique, an improved combination morphological filter (ICMF) is first proposed by using the basic four morphological operations. Then multiscale operation (MICMF) is done by different structural element (SE) lengths chosen by sparse weighted Kurtosis index (SKI w ). The obtained optimized signal is processed through the detrended fluctuation analysis (DFA) which gives the scaling exponent ( λ ) values based on window length. These scaling exponent values are used to classify the PQ disturbances in terms of scatter plots. The performance of the proposed study is calculated by classification accuracy (CA) and relative computational time (RCT) and the efficacy of the proposed technique is validated by comparing the CA and RCT with existing techniques. The entire study is tested on MATLAB/Simulink environment. The complete study is tested and validated using a high-speed multifunction National Instrument USB6008 device for the online monitoring of PQ disturbances on the MATLAB/Simulink environment. • A new adaptive multiscale morphological filter is used for PQ analysis of DC microgrid. • The adaptation of the filter is accomplished using a sparse weighted kurtosis index. • Detrended fluctuation analysis is used to process the optimized signal to yield scaling exponents. • These scaling exponent values are used to classify the PQ disturbances in terms of scatter plots.

Powergram: A novel auto-regressive power spectrumbasedfrequency band selection method and its applicationingearfault diagnosis

Abstract Fast kurtogram (FK) is an effective method for resonance demodulation analysis, which segments the frequency spectrum of raw signal by constructing a tree-shaped band-pass filter bank based on alternate dichotomy or trisection method, then the modulation information is extracted via selecting the maximum kurtosis of components. Though FK has a high computation efficiency, it can not adaptively segment the frequency band due to its 1/3 binary tree structure. Besides, the different characteristics of frequency band filtered signal cannot be comprehensively reflected due to the use of a single kurtosis index in FK. To overcome the above shortcomings of FK, a novel frequency band selection method named Powergram is proposed based on auto-regressive (AR) power spectrum. First of all, the AR power spectrum is computed to overcome the influence of noise and irrelevant components on spectrum segmentation. Second, the minima point nearest to the midpoint of two adjacent maxima is selected as the segmentation boundary, where the maximum segmentation level is preset in advance. Third, a new fusion indicator is constructed by weighting the indexes of kurtosis, correlation coefficient, and spectral negentropy. After that, the frequency band with largest fusion indicator is selected as the optimal demodulation frequency band (ODFB). Then, the envelope spectrum of ODFB filtered signal is analyzed to identify the fault characteristic frequency and complete the fault diagnosis. Finally, Powergram is applied to the simulated and tested data of faulty gear via comparing it with existing FB-based FK and Autogram approaches. The analysis results show that the introduced approach is better than the comparison methods in ODFB selection and fault characteristic extraction.

Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings

The fault vibration signal of rolling bearing is typical non-stationary and non-linear signals. It usually exhibits the characteristics of amplitude modulation and frequency modulation (AM-FM) due to the interaction between the various components within the bearing and modulation of the rotor speed. In addition, the incipient weak fault feature information is inevitably masked by the strong background noise and other vibration interference because of the influence of the working environment of mechanical equipment. Therefore, new high efficiency and effective processing methods are indispensable for feature extraction of bearing fault diagnosis signals. Direct fast iterative filtering (dFIF) is a new kind of adaptive mode decomposition method proposed by Antonio Cicone based on iterative filtering (IF) and fast iterative filtering (FIF). dFIF is used to quickly decompose multi-component signals into a set of intrinsic mode functions (IMFs) by means of fast Fourier transform (FFT). In order to study the decomposition performance and examine the feature extraction ability of the dFIF method, a group of harmonic signals with simple linear superposition, multi-components AM-FM signals with different data length and different signal-to-noise ratio (SNR) are simulated and analyzed. For comparison, other mode decomposition methods were also applied to the simulated signals, such as adaptive local iterative filtering (ALIF), local mean decomposition (LMD), and empirical mode decomposition (EMD). The results show that dFIF decomposition methods have faster decomposition speed and better accuracy than other methods. Furthermore, the results also demonstrate excellent performance in AM-FM feature extraction and anti-mode mixing. On the other hand, the effective weighted sparseness kurtosis (EWSK) index integrates the periodicity and intensity of impact in each mode, which can effectively identify the effective mode. Therefore, considering the above merits, EWSK is integrated on the dFIF to effectively accomplish the fault diagnosis for rolling bearing under strong background noise. Firstly, the raw vibration signals of rolling bearing fault are decomposed into a set of IMFs by using dFIF. The EWSK indicator is then utilized to select IMFs with rich bearing fault impulse information for reconstruction. Finally, the Hilbert envelope demodulation analysis is used to analyze the reconstructed signal, extract the bearing fault feature and then judge the fault type. This proposed method is applied to analyze the simulated and field measured vibration signals of rolling bearing faults. Simultaneously, the other above-mentioned adaptive TFA methods are also adopted to analyze the signals. The results compared show that the proposed method has excellent performance in the aspects of decomposition speed and accuracy under the influence of strong background noise. It demonstrates the effectiveness and applicability of the proposed method for signal processing and fault pattern recognition of rolling bearings.

Series Arc Fault Detection in a Low-Voltage Power System Based on CEEMDAN Decomposition and Sensitive IMF Selection

In the series arc fault detection of a low-voltage distribution network, the features of the fault current signal are easily submerged and arc fault features are difficult to be represented, which greatly increases the difficulty of fault arc detection based on current signals. To solve these problems, a series arc fault detection method combining CEEMDAN decomposition and sensitive IMF selection is proposed. In this paper, the CEEMDAN algorithm is first applied to complete decomposition of the arc current in series faults. Then, 12 feature indicators of the arc current are defined and the frequency band division of the IMF component is realized according to the kurtosis index and energy feature which are more sensitive. The time window-based feature calculation method is proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components is realized by comparing feature indexes such as the variance and root mean square value. Finally, for the current feature set, the second dimension reduction is realized by the subspace transformation algorithm and the series arc fault detection is realized based on the SVM. The actual experiments show that the optimal detection accuracy of the proposed method is 91.67% and the average accuracy of 10 crossvalidation experiments is 88.33%. It shows that the proposed sensitive IMF selection method can effectively capture the fault component signals in the current and the proposed fault feature description method has good representation and discrimination ability.

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods.

An improved Autogram and MOMEDA method to detect weak compound fault in rolling bearings.

When weak compound fault occurs in rolling bearing, the faint fault features suffer from serious noise interference, and different type faults are coupled together, making it a great challenge to separate the fault features. To solve the problems, a novel weak compound fault diagnosis method for rolling bearing based on improved Autogram and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, the kurtosis index in Autogram is modified with multi-scale permutation entropy, and improved Autogram finds the optimal resonance frequency band to preliminarily denoise the weak compound fault signal. Then, MOMEDA is performed to deconvolute the denoised signal to decouple the features of compound fault. Finally, square envelope analysis is applied on the separated deconvoluted signals to identify different type faults according to the fault characteristic frequencies in the spectrums. The proposed method is performed to analyze the simulated signal and experimental datasets of different types of rolling bearing weak compound faults. The results indicate that the proposed method can accurately diagnose the weak compound faults, and comparison with the analysis results of parameter-adaptive variational mode decomposition algorithm verifies its effectiveness and superiority.

Online assessment in two consequent semesters during COVID-19 pandemic: K-means clustering using data mining approach.

Education and assessment have changed during the COVID-19 pandemic so that online courses replaced face-to-face classes to observe the social distance. The quality of online assessments conducted during the pandemic is an important subject to be addressed. In this study, the quality of online assessments held in two consecutive semesters was investigated. One thousand two hundred and sixty-nine multiple-choice online examinations held in the first (n = 535) and second (n = 734) semesters in Birjand University of Medical Sciences during 2020-2021 were examined. Mean, standard deviation, number of questions, skewness, kurtosis, difficulty, and discrimination index of tests were calculated. Data mining was applied using the k-means clustering approach to classify the tests. The mean percentage of answers to all tests was 69.97 ± 19.16, and the number of questions was 34.48 ± 18.75. In two semesters, there was no significant difference between the difficulty of examinations (P = 0.84). However, there was a significant difference in the discrimination index, skewness, and kurtosis of tests (P < 0.001). Moreover, according to the results of the clustering analysis in the first semester, 43% of the tests were very hard, 16% hard, and 7% moderate. In the second semester, 43% were hard, 16% moderate, and 41% easy. To evaluate the tests' quality, calculating difficulty and discrimination indices is not sufficient; many factors can affect the quality of tests. Furthermore, the experience of the first semester had changed characteristics of the second-semester examinations. To enhance the quality of online tests, establishing appropriate rules to hold the examinations and using questions with higher taxonomy are recommended.

Application of nonlinear system identification for EEG modelling using VMD-based deep random vector functional link network

In this paper, the EEG signal is considered for the development of the model. As the signal is nonlinear and non-stationary, the model is designed accordingly which is similar to nonlinear dynamic system identification. Initially, the signal is decomposed by a robust variational mode decomposition method for which the basic noise components are eliminated. Later, a kurtosis index method is applied to choose the best band-limited intrinsic mode functions (BLIMFs) based on their clean coefficient the model is developed using a random vector functional link neural network (RVFLN) for identification. The use of deep RVFLN provides better results as compared to simple RVFLN as explained in the result section. For verification of the system's robustness, three different epileptic signals known as pre-ictal, inter-ictal and ictal are experienced in this piece of work.

A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm

In this paper, a new index is proposed for detecting the frequency of unknown underwater signals based on the stochastic resonance theory. When the received weak signal is input into the stochastic resonance system, first, by frequency analysis, the frequency with the highest amplitude Aₘ of the output signal spectrum is considered as the pre-detection fre-quency. Then a cosine signal with the pre-detection frequency and unit amplitude is constructed. Define the pre-signal-to-noise-ratio as the logarithm of the squared amplitude Aₘ over the mean of signal amplitudes in all other frequencies. The new index is defined as the product of the pre-signal-to-noise-ratio and the correlation coefficient between the received unknown signal and the constructed cosine signal. The new index is featured by taking into account the signal characteristics in both time and frequency domain, and it will yield better signal frequency detection performance. In addition, to improve the time efficiency of the frequency detection, a method to bound the searching range, keyed to the genetic algorithm, of the stochastic resonance system parameters is proposed. The method can be used to detect the frequency of both single frequency and frequency-hopping unknown signals. With the designed new index and system parameter bounding method, the simulations and experiments for the weak underwater unknown signals are conducted. Compared to the piecewise mean value index and weighted power spectral kurtosis index, the new index yields a higher detection probability at varied input signal-to-noise ratios and signal frequencies. With bounding system parameter searching ranges, the time efficiency is improved. The main purpose of this paper is to detect the frequency of unknown underwater weak signals by stochastic resonance system with genetic algorithm. The main contributions are summarized as follows. First, the detection probability of weak signals is improved by stochastic resonance system with the proposed signal detection index than some other indexes. Second, to improve the time efficiency of the signal frequency detection, a method to bound the searching range of system parameters is proposed.

Degree of cyclic target protrusion defined on squared envelope spectrum for rotating machinery fault diagnosis

Rotating machinery fault diagnosis is critical for safe operation of modern mechanical equipment so as to avoid serious damage and economic loss caused by malfunctions. Envelope analysis is an effective method in the field of rotating machinery fault diagnosis, in which the selection of optimal demodulated band containing the most obvious fault features by fault index is a critical step. However, the existing fault indexes are often defined on the basis of global ratio in their domains of definitions, which may result in incorrect band selection. To address the problem, a novel index named degree of cyclic target protrusion (DCTP) for fault diagnosis is proposed in this paper. DCTP is defined on squared envelope spectrum to take adequately fault characteristic frequency (FCF) into consideration. Specifically, Otsu method is utilized to calculate local threshold for determining cyclic target amplitude and cyclic background in two elaborate bands. In this aspect, DCTP is defined as a weighted local ratio of cyclic target amplitude to cyclic background. Because it focuses on FCF, DCTP can avoid interference by extraneous components. DCTP is used to replace the kurtosis index of fast kurtogram (FK) to establish the DCTPgram which is compared with FK and ratio of cyclic content based method in roller bearing fault diagnosis and planetary gearbox fault diagnosis. Experimental results and comparison studies demonstrate that the DCTPgram has a significant advantage for rotating machinery fault diagnosis.

A comparative study of the physical properties for a representative sample of Narrow and Broad-line Seyfert galaxies

ABSTRACT We present a comparative study of the physical properties of a homogeneous sample of 144 Narrow line Seyfert 1 (NLSy1) and 117 Broad-line Seyfert 1 (BLSy1) galaxies. These two samples are in a similar luminosity and redshift range and have optical spectra available in the 16th data release of Sloan Digital Sky Survey (SDSS-DR16) and X-ray spectra in either XMM-NEWTON or ROSAT. Direct correlation analysis and a principal component analysis (PCA) have been performed using ten observational and physical parameters obtained by fitting the optical spectra and the soft X-ray photon indices as another parameter. We confirm that the established correlations for the general quasar population hold for both types of galaxies in this sample despite significant differences in the physical properties. We characterize the sample also using the line shape parameters, namely the asymmetry and kurtosis indices. We find that the fraction of NLSy1 galaxies showing outflow signatures, characterized by blue asymmetries, is higher by a factor of about 3 compared to the corresponding fraction in BLSy1 galaxies. The presence of high iron content in the broad-line region of NLSy1 galaxies in conjunction with higher Eddington ratios can be the possible reason behind this phenomenon. We also explore the possibility of using asymmetry in the emission lines as a tracer of outflows in the inner regions of Active Galactic Nuclei. The PCA results point to the NLSy1 and BLSy1 galaxies occupying different parameter spaces, which challenges the notion that NLSy1 galaxies are a subclass of BLSy1 galaxies.

State degradation evaluation and early fault identification of wind turbine bearings

To solve the shortage of state degradation characteristic and evaluation parameters in the monitoring of the running state of wind turbine bearings, and the low accuracy of early fault identification, a state degradation characteristic index, which combines multi-scale weighted permutation entropy (MWPE) and local linear embedding algorithm (LLE), is proposed for performance degradation monitoring and early fault identification of wind turbine bearings. The degradation index is quantitatively evaluated by four evaluation parameters: the initial time of state degradation, the sensitivity of initial degradation, the failure mutability and the consistency of degradation trend. Through the full cycle acceleration life monitoring data of the bearings, the result show that the proposed MWPE LLE degradation index can effectively characterize the whole process of bearing life cycle from stable operation to degradation to failure. Compared with the quantitative evaluation result of the kernel principal component analysis (KPCA), weighted permutation entropy (WPE), the root mean square (RMS) and kurtosis index, the comprehensive expression ability of MWPE-LLE degradation index is better. It can not only accurately capture the mutation time of state degradation, but also detect the initial degradation time in advance of the four kinds of characteristic indexes, and accurately judge the failure type of the early failure of the test bearing, and the judgment result is consistent with the inspection result after the experiment.

Effective fault distance estimation and diagnosis in the PV based DC‐ring network using Hilbert Huang transform and weighted online sequential random vector functional network

This paper proposes a novel approach for fault detection, classification, and distance estimation of five kinds of faults (ie, cable and Arc faults) in the PV-based DC-ring microgrid. The proposed approach is based on the Hilbert Huang Transform (HHT) and online sequential random vector functional link network (OS-RVFLN). Initially, differential current signals are passed through empirical mode decomposition (EMD) to yield intrinsic mode functions (IMFs). Among the series of IMFs, more efficient IMFs are selected based on the threshold of maximum weighted kurtosis index (M-WKI) value for fault diagnosis. The selected IMFs are passed through Hilbert transform (HT) for determining the instantaneous frequency, magnitude spectrum, and Hilbert energy. Then, the Teager energy operator (TEO) is applied to the more effective IMF among the selected IMFs of each DC fault for fault detection. The most suitable target features are extracted from the selected efficient IMFs. This feature set data is initialized to the proposed HHT-OSRVFLN algorithm for five kinds of faults classification and its location estimation. The performance of the proposed algorithm in the proposed microgrid is proved in terms of low fault detection time, superior classification accuracy, and low relative error through different case studies by consuming minimum computation time in MATLAB/Simulink environment. Finally, the digital architecture of the proposed method is designed and simulated in Xilinx ISE 14.5 software environment to estimate the fault location. The Chip scope hardware debugging output proves the correctness, simplicity, feasibility, practicability, and robustness of the proposed method for online fault diagnosis.

Adaptive stochastic resonance quantified by a novel evaluation index for rotating machinery fault diagnosis

Stochastic resonance (SR) provides the enhancement of fault characteristic signals with the assistance of noise. The performance of adaptive SR must be evaluated using an appropriate index to automatically enhance various characteristic signals. This paper proposes a weighted impulse (WI) index to evaluate the performance of the adaptive general scale transformation SR (AGSTSR) in rotating machinery fault diagnosis and uses an intelligent optimization algorithm to obtain the optimal WI. The comparison results in the simulation experiment revealed that the proposed WI-based AGSTSR method presented the fault characteristics more clearly than the adaptive SR method based on the impulse index or weighted kurtosis index. Moreover, the proposed method offered the best anti-noise performance in the simulation experiment. Finally, two experimental case studies validated that the proposed method can adaptively realize early fault diagnosis of rotating machinery through the analysis of weak fault characteristics.

Evaluation of kurtosis on hearing loss caused by non-steady state noise in general equipment manufacturing industry

Objective: To study the effect of occupational exposure to non-steady state noise on hearing loss in the general equipment manufacturing industry, and to explore the feasibility of applying kurtosis index to evaluate hearing loss caused by non-steady state noise. Methods: A total of 233 workers exposed to non-steady state noise in 6 general equipment manufacturing enterprises were selected as the observation group, and 237 workers exposed to steady noise in 4 textile enterprises were selected as the control group between 2012 and 2018. Personal normalized continuous A-weighted sound pressure level equivalent to an 8 h-working-day (L(Aeq, 8 h)) was determined by a noise dosimeter. Cumulation noise exposure (CNE) was calculated from L(Aeq, 8 h) and related working age, and the CNE was adjusted by using noise kurtosis (CNE' after adjustment) . Meanwhile, questionnaires and hearing loss tests were performed for all subjects. The changes in the threshold of high-frequency hearing and the incidence of high-frequency hearing loss caused by noise in the two groups were compared before and after the adjustment of the kurtosis of CNE. The correlation between CNE and the incidence of high-frequency hearing loss before and after kurtosis adjustment was analyzed. Results: There was an difference between the 3000-8000 Hz hearing threshold of workers in different CNE groups (P<0.05) . logistic regression analysis showed that CNE was a risk factor of high-frequency hearing loss for workers in the observation group (OR=1.189, P<0.05) , and trend Chi-square test showed that the incidence of high-frequency hearing loss increased with CNE levels (χ(2)(trend)=34.415, P<0.05) .Before the kurtosis adjustment, in the CNE 95~<110 dB (A) ·year level group, the incidence of high-frequency hearing loss and the high-frequency hearing threshold in the observation group were significantly higher than those in the control group (P<0.05) . After kurtosis adjustment, there was no significant difference in the incidence of high-frequency hearing loss between the observation group and the control group in each level group (P>0.05) . The correlation between CNE after kurtosis adjustment and the incidence of high-frequency hearing loss was better than that before adjustment (after R(2) adjustment >before R(2) adjustment) . Conclusion: The effect of non-steady state noise on high-frequency hearing loss of workers in general equipment industry increases with the increase of CNE, and the effect of non-steady state noise on high-frequency hearing loss of workers is greater than that of steady noise. CNE' can be used to evaluate the hearing loss caused by non-steady state noise.

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project.

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project—a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy.

The working environment of wind turbine gearboxes is complex, complicating the effective monitoring of their running state. In this paper, a new gearbox fault diagnosis method based on improved variational mode decomposition (IVMD), combined with time-shift multi-scale sample entropy (TSMSE) and a sparrow search algorithm-based support vector machine (SSA-SVM), is proposed. Firstly, a novel algorithm, IVMD, is presented for solving the problem where VMD parameters (K and α) need to be selected in advance, which mainly contains two steps: the maximum kurtosis index is employed to preliminarily determine a series of local optimal decomposition parameters (K and α), then from the local parameters, the global optimum parameters are selected based on the minimum energy loss coefficient (ELC). After decomposition by IVMD, the raw signal is divided into K intrinsic mode functions (IMFs), the optimal IMF(s) with abundant fault information is (are) chosen based on the minimum envelopment entropy criterion. Secondly, the time-shift technique is introduced to information entropy, the time-shift multi-scale sample entropy algorithm is applied for the analysis of the complexity of the chosen optimal IMF and extract fault feature vectors. Finally, the sparrow search algorithm, which takes the classification error rate of SVM as the fitness function, is used to adaptively optimize the SVM parameters. Next, the extracted TSMSEs are input into the SSA-SVM model as the feature vector to identify the gear signal types under different conditions. The simulation and experimental results confirm that the proposed method is feasible and superior in gearbox fault diagnosis when compared with other methods.

Probability mapping based artifact detection and removal from single-channel EEG signals for brain–computer interface applications

BackgroundDifferent types of artifacts in the electroencephalogram (EEG) signals can considerably reduce the performance of the later-stage EEG analysis algorithms for making decisions, such as those for brain–computer interfacing (BCI) classification. In this paper, we address the problem of artifact detection and removal from single-channel EEG signals. New methodWe propose a novel approach that maps the probability of an EEG epoch to be artifactual based on four different statistical measures: entropy (a measure of uncertainty), kurtosis (a measure of peakedness), skewness (a measure of asymmetry), and periodic waveform index (a measure of periodicity). Then, a stationary wavelet transform based artifact removal is proposed that employs a particular probability threshold provided by the user. ResultsWe have executed our experiments with both synthetic and real EEG data. It is observed that the proposed method exhibits a superior performance for suppressing the artifact contaminated from EEG with minimum distortion. Moreover, evaluation of the algorithm using EEG dataset for BCI experiments reveals that artifact removal can considerably improve the BCI output in both event-related potential and motor-imagery based BCI applications. Comparison with existing methodsThe proposed algorithm has been applied to both real and synthesized data testing and compared with other state-of-the-art automated artifact removal methods. Its superior performance is verified in terms of various performance metrics including computational complexity for justifying its use in BCI-like real-time applications. ConclusionOur work is expected to be useful for future research EEG signal processing and eventually to develop more accurate real-time EEG-based BCI applications.