Статті в журналах: "Kurtosis method"

1

Peng, Yiming, and Yang Li. "Study of the Windowing and Overlap-Add Operation for a Super-Gaussian Random Vibration Test." Shock and Vibration 2021 (May 28, 2021): 1–18. http://dx.doi.org/10.1155/2021/6644957.

Повний текст джерела

Анотація:

Random vibration environmental testing employs the specified statistical properties of the real world vibration to reproduce the desired excitations on the shaker table for fatigue test purposes. Smooth and safe operation is the essential requirement for a long-duration test. Traditionally, the windowing and overlap-add (WOA) method is applied to the acceleration signals of the shaker table, and previous studies have indicated that this operation reduces the kurtoses of the processed signals. To protect the test equipment from abrupt changes in the input voltage, the WOA method is proposed to operate on the input voltage signals in a frame-by-frame form for super-Gaussian environmental testing. To figure out the impacts of the proposed operation on the response kurtoses of a shaker table, we express the system transfer function in the time domain, and the WOA method is analysed considering the transfer function of a dynamic system. Based on the analysis, a further study is made to explain the mechanism of the kurtosis decrease due to the WOA method. Through the study, we find that the kurtosis reduction conclusion is not applicable to all types of super-Gaussian signals, and the kurtoses can be invariable and even increased by allocating the positions of the high-excursion peaks of super-Gaussian signals when the WOA method is applied. A window function is recommended for zero-memory nonlinear (ZMNL) transformation to move the positions of the high-excursion peaks of a super-Gaussian signal, providing a novel way of adjusting kurtosis when WOA method is applied. The proposed WOA method and window function are first verified in a single-input-single-output (SISO) numerical simulation to test their effectiveness under different reference kurtoses. Then, they are evaluated in a two-input-two-output shaker table test. The test results demonstrate that the proposed window function can prevent the kurtosis decrease with the application of the WOA method.

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Yang, Yanli, and Ting Yu. "An Adaptive Spectral Kurtosis Method Based on Optimal Filter." Shock and Vibration 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6987250.

Повний текст джерела

Анотація:

As a useful tool to detect protrusion buried in signals, kurtosis has a wide application in engineering, for example, in bearing fault diagnosis. Spectral kurtosis (SK) can further indicate the presence of a series of transients and their locations in the frequency domain. The factors influencing kurtosis values are first analyzed, leading to the conclusion that amplitude, not the frequency of signals, and noise make major contribution to kurtosis values. It is helpful to detect impulsive components if the components with big amplitude are removed from composite signals. Based on this cognition, an adaptive SK algorithm is proposed in this paper. The core steps of the proposed SK algorithm are to find maxima, add window around maxima, merge windows in the frequency domain, and then filter signals according to the merged window in the time domain. The parameters of the proposed SK algorithm are varying adaptively with signals. Some experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Headrick, Todd C., and Mohan D. Pant. "A Doubling Method for the Generalized Lambda Distribution." ISRN Applied Mathematics 2012 (May 7, 2012): 1–19. http://dx.doi.org/10.5402/2012/725754.

Повний текст джерела

Анотація:

This paper introduces a new family of generalized lambda distributions (GLDs) based on a method of doubling symmetric GLDs. The focus of the development is in the context of L-moments and L-correlation theory. As such, included is the development of a procedure for specifying double GLDs with controlled degrees of L-skew, L-kurtosis, and L-correlations. The procedure can be applied in a variety of settings such as modeling events and Monte Carlo or simulation studies. Further, it is demonstrated that estimates of L-skew, L-kurtosis, and L-correlation are substantially superior to conventional product-moment estimates of skew, kurtosis, and Pearson correlation in terms of both relative bias and efficiency when heavy tailed distributions are of concern.

Стилі APA, Harvard, Vancouver, ISO та ін.

4

R, Lakshmi, and T. A. Sajesh. "Robust Quadratic Discriminant Analysis using Kurtosis Method." Journal of Computer and Mathematical Sciences 9, no. 12 (December 4, 2018): 1907–14. http://dx.doi.org/10.29055/jcms/937.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Song, Jinlong, Zhiyong Shi, Lvhua Wang, and Hailiang Wang. "Random Error Analysis of MEMS Gyroscope Based on an Improved DAVAR Algorithm." Micromachines 9, no. 8 (July 27, 2018): 373. http://dx.doi.org/10.3390/mi9080373.

Повний текст джерела

Анотація:

In view that traditional dynamic Allan variance (DAVAR) method is difficult to make a good balance between dynamic tracking capabilities and the confidence of the estimation. And the reason is the use of a rectangular window with the fixed window length to intercept the original signal. So an improved dynamic Allan variance method was proposed. Compared with the traditional Allan variance method, this method can adjust the window length of the rectangular window adaptively. The data in the beginning and terminal interval was extended with the inverted mirror extension method to improve the utilization rate of the interval data. And the sliding kurtosis contribution coefficient and kurtosis were introduced to adjust the length of the rectangular window by sensing the content of shock signal in terminal interval. The method analyzed the window length change factor in different stable conditions and adjusted the rectangular window’s window length according to the kurtosis, sliding kurtosis contribution coefficient. The test results show that the more the kurtosis stability threshold was close to 3, the stronger the dynamic tracking ability of DAVAR would be. But the kurtosis stability threshold was too close to 3, there was a misjudgement in kurtosis analysis of signal stability, resulting in distortion of DAVAR analysis results. When using the improved DAVAR method, the kurtosis stability threshold can be close to 3 to improve the tracking ability and the estimation confidence in stable interval. Therefore, it solved the problem that the dynamic Allan variance tracking ability and confidence level were difficult to take into account, and also solved the problem of misjudgement in the stability analysis of kurtosis.

Стилі APA, Harvard, Vancouver, ISO та ін.

6

Li, Weihan, Yang Li, Ling Yu, Jian Ma, Lei Zhu, Lingfeng Li, Huayue Chen, and Wu Deng. "A Novel Fault Feature Extraction Method for Bearing Rolling Elements Using Optimized Signal Processing Method." Applied Sciences 11, no. 19 (September 29, 2021): 9095. http://dx.doi.org/10.3390/app11199095.

Повний текст джерела

Анотація:

A rolling element signal has a long transmission path in the acquisition process. The fault feature of the rolling element signal is more difficult to be extracted. Therefore, a novel weak fault feature extraction method using optimized variational mode decomposition with kurtosis mean (KMVMD) and maximum correlated kurtosis deconvolution based on power spectrum entropy and grid search (PGMCKD), namely KMVMD-PGMCKD, is proposed. In the proposed KMVMD-PGMCKD method, a VMD with kurtosis mean (KMVMD) is proposed. Then an adaptive parameter selection method based on power spectrum entropy and grid search for MCKD, namely PGMCKD, is proposed to determine the deconvolution period T and filter order L. The complementary advantages of the KMVMD and PGMCKD are integrated to construct a novel weak fault feature extraction model (KMVMD-PGMCKD). Finally, the power spectrum is employed to deal with the obtained signal by KMVMD-PGMCKD to effectively implement feature extraction. Bearing rolling element signals of Case Western Reserve University and actual rolling element data are selected to prove the validity of the KMVMD-PGMCKD. The experiment results show that the KMVMD-PGMCKD can effectively extract the fault features of bearing rolling elements and accurately diagnose weak faults under variable working conditions.

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Xu, Yonggang, Zeyu Fan, Kun Zhang, and Chaoyong Ma. "A Novel Method for Extracting Maximum Kurtosis Component and Its Applications in Rolling Bearing Fault Diagnosis." Shock and Vibration 2019 (August 25, 2019): 1–17. http://dx.doi.org/10.1155/2019/8218237.

Повний текст джерела

Анотація:

Rolling bearing plays an important role in the overall operation of the mechanical system; therefore, it is necessary to monitor and diagnose the bearings. Kurtosis is an important index to measure impulses. Fast Kurtogram method can be applied to the fault diagnosis of rolling bearings by extracting maximum kurtosis component. However, the final result may disperse the effective fault information to different frequency bands or find wrong frequency band, resulting in inaccurate frequency band selection or misdiagnosis. In order to find the maximum component of kurtosis accurately, an algorithm of frequency band multidivisional and overlapped based on EWT (MDO-EWT) is proposed in this paper. This algorithm changes the traditional Fast Kurtogram frequency bands division method and filtering method. It builds the EWT boundaries based on the maximum kurtosis component in each iteration and finally obtains the maximum kurtosis component. Through the simulation signal and the rolling bearing inner and outer ring fault signals verification, it is proved that the proposed method has a good performance on accuracy and effectiveness.

Стилі APA, Harvard, Vancouver, ISO та ін.

8

Yang, Rui, Hongkun Li, Chaoge Wang, and Changbo He. "Rolling element bearing weak feature extraction based on improved optimal frequency band determination." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 2 (March 22, 2018): 623–34. http://dx.doi.org/10.1177/0954406218761487.

Повний текст джерела

Анотація:

Conventional Kurtosis method represents the statistical property of signal in the time domain. Correlated Kurtosis is proposed that combines the correlation coefficient and Kurtosis in order to indicate the periodicity and impact of signal. In this study, correlated Kurtosis is introduced into frequency domain to improve the recognition accuracy of the optimal frequency band. It does not perform well under the lower signal-to-noise ratio. And then, maximum correlation Kurtosis de-convolution method is used for extracting the approximate impact signal before selecting the optimal frequency band. However, it is limited in diagnosing rolling element bearing fault in the case of the algorithm iteration period is unknown. In addition, filter length also affects the filtering results. To eliminate the confusion, correlated Kurtosis of the frequency domain is applied to iteration period calculation. In this research, a new index is also proposed based on entropy and correlated Kurtosis to optimize the filter length. Then, the full bandwidth of filtered signal is partitioned into several sub-bands according to the refined wavelet packet binary tree. The correlated Kurtosis for each sub-band is calculated. The optimal sub-band for which the correlated Kurtosis is maximal is extracted to analysis. In the end, the efficiency of the new index and the fault diagnosis method are verified by using simulation data and experimental data.

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Jin, Yan, Zezong Chen, Lingang Fan, and Chen Zhao. "Spectral Kurtosis–Based Method for Weak Target Detection in Sea Clutter by Microwave Coherent Radar." Journal of Atmospheric and Oceanic Technology 32, no. 2 (February 2015): 310–17. http://dx.doi.org/10.1175/jtech-d-13-00108.1.

Повний текст джерела

Анотація:

AbstractA new method is proposed to detect small targets embedded in sea clutter for land-based microwave coherent radar using spectral kurtosis as a signature from radar data. It is executed according to the following procedures. First, the echoes of radar from each range gate are processed by the technique of short-time Fourier transform. Then, the kurtosis of each Doppler channel is estimated from the time–Doppler spectra. Last, the spectral kurtosis is compared to a threshold to determine whether a target exists. The proposed method is applied to measured datasets of different sea conditions from slight to moderate. The signal from a small boat is detected successfully. Furthermore, the detection performance of the proposed method is analyzed by the way of Monte Carlo simulation. It demonstrates that the spectral kurtosis–based detector works well for weak target detection when the target’s Doppler frequency is beyond the strong clutter region.

Стилі APA, Harvard, Vancouver, ISO та ін.

10

Honarvar, F., and H. R. Martin. "New Statistical Moments for Diagnostics of Rolling Element Bearings." Journal of Manufacturing Science and Engineering 119, no. 3 (August 1, 1997): 425–32. http://dx.doi.org/10.1115/1.2831123.

Повний текст джерела

Анотація:

Statistical moment analysis has proven to be a very effective technique for diagnosis of rolling element bearings. The fourth normalized central statistical moment, kurtosis, has been the major parameter in this method. In this paper it will be shown that the third normalized statistical moment can be as effective as kurtosis if the data is initially rectified. The advantage of this moment over the traditional kurtosis value is its lesser susceptibility to spurious vibrations, which is considered to be one of the shortcomings of higher statistical moments including kurtosis. The sensitivity of this moment to changes of load and speed is also less than kurtosis. The proposed method can also be applied to higher odd statistical moments.

Стилі APA, Harvard, Vancouver, ISO та ін.

11

Blais, Brian S., N. Intrator, H. Shouval, and Leon N. Cooper. "Receptive Field Formation in Natural Scene Environments: Comparison of Single-Cell Learning Rules." Neural Computation 10, no. 7 (October 1, 1998): 1797–813. http://dx.doi.org/10.1162/089976698300017142.

Повний текст джерела

Анотація:

We study several statistically and biologically motivated learning rules using the same visual environment: one made up of natural scenes and the same single-cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the Bienenstock-Cooper-Munro (BCM) learning rule, and single-cell independent component analysis. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

Стилі APA, Harvard, Vancouver, ISO та ін.

12

Hong, Hoonbin, and Ming Liang. "K-Hybrid: A Kurtosis-Based Hybrid Thresholding Method for Mechanical Signal Denoising." Journal of Vibration and Acoustics 129, no. 4 (April 12, 2007): 458–70. http://dx.doi.org/10.1115/1.2748467.

Повний текст джерела

Анотація:

This paper presents a kurtosis-based hybrid thresholding method, K-hybrid, for denoising mechanical fault signals. The threshold used in the hybrid thresholding method is determined based on kurtosis, which is an important indicator of the signal-to-noise ratio (SNR) of a signal. This together with its sensitivity to outliers and data-driven nature makes a kurtosis-based threshold particularly suitable for on-line detection of mechanical faults featuring impulsive signals. To better reflect the signal composition, the proposed hybrid thresholding rule divides the wavelet transformed input signals into four zones associated with different denoising actions. This alleviates the difficulties present in the simple keep-or-remove and shrink-or-remove approaches adopted by the hard- and soft-thresholding rules. The boundaries of the four zones are on-line adjusted in response to the kurtosis change of the signal. Our simulation results suggest that the mean squared error (MSE) is unable to distinguish the results in terms of the amount of falsely identified impulses. It is therefore inappropriate to use MSE alone for evaluating the denoising results of mechanical signals. As such, a combined criterion incorporating both MSE and false identification power Pfalse is proposed. Our analysis has shown that the proposed K-hybrid approach outperforms the soft, hard, and BayesShrink thresholding methods in terms of the combined criterion. It also compares favorably to the MAP thresholding method for signals with low kurtosis or low SNR. The proposed approach has been successfully applied to noise reduction and fault feature extraction of bearing signals.

Стилі APA, Harvard, Vancouver, ISO та ін.

13

Dadkhah Laleh, Alireza, Mirmohammad Ettefagh, and Reza Hasanezhad Qadim. "Comparison of beta-kurtosis and kurtosis methods for troubleshooting the performance of a transmission vehicle using vibrating frequencies." International Journal of Engineering & Technology 7, no. 2.13 (April 15, 2018): 314. http://dx.doi.org/10.14419/ijet.v7i2.13.13068.

Повний текст джерела

Анотація:

One of the main methods in maintenance and repair is a preventive maintenance method that is often more effective. The requirements of this method are to monitor the performance of machinery during operation. One of the car's functions that is monitored in this way is its vibra-tions. In this paper, a mathematical model of vibration analysis of a passenger car gearbox is presented based on Beta-Kurtosis and Kurtosis methods. In the next step, the data and test settings for the gearbox are based on the accelerometer installation to record the vibrations of the gearbox. To verify the accuracy of the proposed method, the results of the vibrational analysis of the car gearbox in four modes of a healthy gearbox, defective gearbox in the shaft end bearing, gear shaft failure on the gear shaft, simultaneous failure of the bearing and gearbox on the gear shaft were compared. Also, the results are compared for both Kurtosis and Beta-Kurtosis methods. The results show that both of the proposed methods are very accurate in identifying faults in the gearbox and determining the type of fault.

Стилі APA, Harvard, Vancouver, ISO та ін.

14

Cui, Zhitao, Yongcai Zhang, and Niu Yi. "Optimization of Kurtosis in the Extend-Infomax Blind Signal Separation Algorithm." Mobile Information Systems 2021 (December 10, 2021): 1–8. http://dx.doi.org/10.1155/2021/3902271.

Повний текст джерела

Анотація:

A kurtosis optimization method is proposed to improve the blind separated signal qualities based on the extend-infomax algorithm. The kurtosis of the hypothetical source signal was optimized based on the probability density function of sub-Gaussian signals. Obtained parameters after kurtosis optimization were then utilized to validate the effectiveness of the algorithm, which showed that the running time of the algorithm was significantly reduced, and the qualities of the separated signals were enhanced. Methods. Using kurtosis as a control variable, a one-way analysis of variance (ANOVA) was carried out on the algorithm’s performance metrics, the number of iterations, and the signal-to-noise ratio of the separated signal. Results. The results showed that there were significant differences in the above metrics under different kurtosis levels. The curves of average metric values indicate that, with the increase in kurtosis of the hypothetical source signal, the performance of the algorithm was improved.

Стилі APA, Harvard, Vancouver, ISO та ін.

15

Li, Yong, Gang Cheng, Xihui Chen, and Yusong Pang. "Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM." Entropy 21, no. 10 (October 22, 2019): 1025. http://dx.doi.org/10.3390/e21101025.

Повний текст джерела

Анотація:

As the supporting unit of rotating machinery, bearing can ensure efficient operation of the equipment. Therefore, it is very important to monitor the status of bearings accurately. A bearing fault diagnosis mothed based on Multipoint Optimal Minimum Local Mean Entropy Deconvolution Adjusted (MOMLMEDA) and Long Short-Term Memory (LSTM) is proposed. MOMLMEDA is an improved algorithm based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). By setting the local kurtosis mean as a new selection criterion, it can effectively avoid the interference of false kurtosis caused by noise and improve the accuracy of optimal kurtosis position. The optimal filter designed by optimal kurtosis position has periodic and amplitude characteristics, which are used as the fault feature in this paper. However, this feature has temporal characteristics and cannot be used as input of general neural network directly. LSTM is selected as the classification network in this paper. It can effectively avoid the influence of the temporal problem existing in feature vectors. Accurate diagnosis of bearing faults is realized by training classification neural network with samples. The overall recognition rate is up to 93.50%.

Стилі APA, Harvard, Vancouver, ISO та ін.

16

Smallwood, David. "Vibration with Non-Gaussian Noise." Journal of the IEST 52, no. 2 (October 1, 2009): 13–30. http://dx.doi.org/10.17764/jiet.52.2.gh0444564n8765k1.

Повний текст джерела

Анотація:

Three methods are introduced for generating realizations of time histories with a specified auto-spectral density while controlling the kurtosis. One of the methods also allows the skewness to be specified. A second method allows large excursions (that produce large kurtosis) to be randomly distributed or almost periodic. In addition, the second method allows the average number of large excursions per unit of time to be specified. All the methods are variations of the inverse Welch method. The shape of the discrete Fourier magnitude is specified for a frame of data, thus controlling the shape of the auto-spectral density. The phase of the frame of data or the magnitude of the amplitude spectrum is modified to control the kurtosis or the skewness. The frames of data are multiplied by a window and overlapped and added to produce the realization.

Стилі APA, Harvard, Vancouver, ISO та ін.

17

Zhu, Ke Heng, Xi Geng Song, and Dong Xin Xue. "Roller Bearing Fault Diagnosis Based on IMF Kurtosis and SVM." Advanced Materials Research 694-697 (May 2013): 1160–66. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.1160.

Повний текст джерела

Анотація:

This paper presents a fault diagnosis method of roller bearings based on intrinsic mode function (IMF) kurtosis and support vector machine (SVM). In order to improve the performance of kurtosis under strong levels of background noise, the empirical mode decomposition (EMD) method is used to decompose the bearing vibration signals into a number of IMFs. The IMF kurtosis is then calculated because of its sensitivity of impulses caused by faults. Subsequently, the IMF kurtosis values are treated as fault feature vectors and input into SVM for fault classification. The experimental results show the effectiveness of the proposed approach in roller bearing fault diagnosis.

Стилі APA, Harvard, Vancouver, ISO та ін.

18

Anderson, David A., and Theodore F. Argo. "Kurtosis loss as a metric for hearing protection evaluation in impulsive noise environments." JASA Express Letters 2, no. 3 (March 2022): 033603. http://dx.doi.org/10.1121/10.0009659.

Повний текст джерела

Анотація:

Hearing loss standards depend on noise power and duration but are incomplete when the noise is primarily impulsive in nature rather than maintaining a continuous power level. Calculating the kurtosis of a noise exposure captures information about its impulsivity, and high kurtosis values cause additional hearing damage. In this paper, a method for measuring the reduction of noise kurtosis through hearing protection is outlined, and measurements demonstrate that spectral insertion loss is independent of the noise kurtosis and that kurtosis loss is not related to either the mean or standard deviation of spectral attenuation.

Стилі APA, Harvard, Vancouver, ISO та ін.

19

Kim, Sol. "Which One is More Important Factor for Pricing Options, Skewness or Kurtosis?" Journal of Derivatives and Quantitative Studies 14, no. 2 (November 30, 2006): 25–50. http://dx.doi.org/10.1108/jdqs-02-2006-b0002.

Повний текст джерела

Анотація:

This paper investigates the relative importance of the skewness and kurtosis of the risk neutral distribution for pricing KOSPI200 options. The skewness and kurtosis are estimated from non parametric method of Bakshi, Kapadia, and Madan (2003) and the parametric method of Corrado and Su (1996). We show that the skewness of the risk neutral distribution is more important factor than the kurtosis irrespective of the estimation method, the definition of pricing errors, the moneyness, the type of options and a period of time.

Стилі APA, Harvard, Vancouver, ISO та ін.

20

Zhang, Yaojie, Yu Wei, and Benshan Shi. "The pricing of loan insurance based on the Gram-Charlier option model." China Finance Review International 8, no. 4 (November 19, 2018): 425–40. http://dx.doi.org/10.1108/cfri-10-2017-0210.

Повний текст джерела

Анотація:

PurposeThe purpose of this paper is to develop a loan insurance pricing model allowing for the skewness and kurtosis existing in underlying asset returns.Design/methodology/approachUsing the theory of Gram-Charlier option, the authors first derive a closed-form solution of the Gram-Charlier pricing model. To address the difficulties in implementing the pricing model, the authors subsequently propose an iterative method to estimate skewness and kurtosis in practical application, which shows a relatively fast convergence rate in the empirical test.FindingsNot only the theoretical analysis but also the empirical evidence shows that the effects of skewness and kurtosis on loan insurance premium tend to be negative and positive, respectively. Furthermore, the actual values of skewness and kurtosis are usually negative and positive, respectively, which leads to the empirical result that the pricing model ignoring skewness and kurtosis substantially underestimates loan insurance premium.Originality/valueThis paper proposes a loan insurance pricing model considering the skewness and kurtosis of asset returns, in which the authors use the theory of Gram-Charlier option. More importantly, the authors further propose a novel iterative method to estimate skewness and kurtosis in practical application. The empirical evidence suggests that the Gram-Charlier pricing model captures the information content of skewness and kurtosis.

Стилі APA, Harvard, Vancouver, ISO та ін.

21

Xiao, Dongming, Jiakai Ding, Xuejun Li, and Liangpei Huang. "Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network." Applied Sciences 9, no. 24 (December 11, 2019): 5424. http://dx.doi.org/10.3390/app9245424.

Повний текст джерела

Анотація:

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.

Стилі APA, Harvard, Vancouver, ISO та ін.

22

Wang, Xiao Lin, Wei Hua Han, Han Gu, Cun Hu, and Xing Xing Han. "Research on Rolling Element Bearing Fault Diagnosis Based on EEMD and Correlated Kurtosis." Applied Mechanics and Materials 680 (October 2014): 198–205. http://dx.doi.org/10.4028/www.scientific.net/amm.680.198.

Повний текст джерела

Анотація:

In order to extract the faint fault information from complicated vibration signal of bearing, the correlated kurtosis is introduced into the field of rolling bearing fault diagnosis. Combined with ensemble empirical mode decomposition (EEMD) and correlated kurtosis, a feature extraction method is proposed. According to the method, by EEMD processing a group of intrinsic mode functions (IMFs) are obtained, then the IMF with maximal correlated kurtosis is selected, and the weak fault signal is clearly extracted. The effectiveness of the method is demonstrated on both simulated signal and actual data.

Стилі APA, Harvard, Vancouver, ISO та ін.

23

Chen, Xiaohui, Lei Xiao, Xinghui Zhang, and Zhenxiang Liu. "A heterogeneous fault diagnosis method for bearings in gearbox." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 8 (July 27, 2014): 1491–99. http://dx.doi.org/10.1177/0954406214544727.

Повний текст джерела

Анотація:

Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.

Стилі APA, Harvard, Vancouver, ISO та ін.

24

Wang, He, and Yunhong Xin. "Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background." Sensors 20, no. 3 (January 30, 2020): 755. http://dx.doi.org/10.3390/s20030755.

Повний текст джерела

Анотація:

Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.

Стилі APA, Harvard, Vancouver, ISO та ін.

25

Suhariadi, Iping, Masaharu Shiratani, and Naho Itagaki. "Morphology Evolution Of ZnO Thin Films Deposited By Nitrogen Mediated Crystallization Method." MATEC Web of Conferences 159 (2018): 02031. http://dx.doi.org/10.1051/matecconf/201815902031.

Повний текст джерела

Анотація:

We study the surface morphology of ZnO thin films deposited by nitrogen mediated crystallization method utilizing atomic force microscopy as a function of nitrogen flow rates. Initially, the surface morphology of ZnO thin film deposited without nitrogen exhibits a bumpy surface with spiky grains where the skewness and kurtosis values were found to be 0.48 and 4.80, respectively. By addition of small amount of nitrogen, the skewness and kurtosis values of the films significantly decrease associated with a flatter topography. Further increase in nitrogen flow rate to 16 sccm has roughened the surface shown mainly by the increase in kurtosis value to be 3.30. These results indicate that the addition of small amount of nitrogen during deposition process has enhanced the adatoms migration on the surface resulting in a superior film with a larger grain size. Two-dimensional power spectral density analysis reveals that all the films have self-affine fractal geometry with total fractal values in the range of 2.14 to above 3.00.

Стилі APA, Harvard, Vancouver, ISO та ін.

26

Zhang, Xinghui, Jianshe Kang, Lei Xiao, Jianmin Zhao, and Hongzhi Teng. "A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis." Shock and Vibration 2015 (2015): 1–22. http://dx.doi.org/10.1155/2015/385412.

Повний текст джерела

Анотація:

A new improved Kurtogram was proposed in this paper. Instead of Kurtosis, correlated Kurtosis of envelope signal extracted from the wavelet packet node was used as an indicator to determine the optimal frequency band. Correlated Kurtosis helps to determine the fault related impulse signals not affected by other unrelated signal components. Finally, two simulated and three experimental bearing fault cases are used to validate the effectiveness of proposed method and to compare with other similar methods. The results demonstrate it can locate resonant frequency band with a high reliability than two previous developed methods by Lei et al. and Wang et al. especially for the incipient faults under low load.

Стилі APA, Harvard, Vancouver, ISO та ін.

27

Liu, Zhi Chuan, Li Wei Tang, and Li Jun Cao. "Feature Extraction Method for Rolling Bearing’s Week Fault Based on Kalman Filter and FSK." Applied Mechanics and Materials 574 (July 2014): 684–89. http://dx.doi.org/10.4028/www.scientific.net/amm.574.684.

Повний текст джерела

Анотація:

Aiming at the problem that traditional demodulated resonance technology has the deficiency of difficulty to choose the parameters of band-pass filter, Kalman filter technology and fast spectral kurtosis were combined for fault feature extraction of rolling bearing. AR model was firstly built with gearbox original vibration signals, and then model order was ascertained with AIC formula, and finally model parameters were calculated with least-squares method. The original signals were pretreated by Kalman filter. Fast spectral kurtosis (FSK) was used to choose parameters of the best band-pass filter, and finally fault diagnosis was achieved by the energy operator demodulation spectrum analysis of band-pass filtered signal. The analysis result of engineering signals indicated that fault feature extraction method based on Kalman filter and fast spectral kurtosis can primely provide a new feature extraction method for rolling bearing’s week fault.

Стилі APA, Harvard, Vancouver, ISO та ін.

28

Headrick, Todd C., and Mohan D. Pant. "A Method for Simulating Nonnormal Distributions with Specified L-Skew, L-Kurtosis, and L-Correlation." ISRN Applied Mathematics 2012 (September 25, 2012): 1–23. http://dx.doi.org/10.5402/2012/980827.

Повний текст джерела

Анотація:

This paper introduces two families of distributions referred to as the symmetric κ and asymmetric - distributions. The families are based on transformations of standard logistic pseudo-random deviates. The primary focus of the theoretical development is in the contexts of L-moments and the L-correlation. Also included is the development of a method for specifying distributions with controlled degrees of L-skew, L-kurtosis, and L-correlation. The method can be applied in a variety of settings such as Monte Carlo studies, simulation, or modeling events. It is also demonstrated that estimates of L-skew, L-kurtosis, and L-correlation are superior to conventional product-moment estimates of skew, kurtosis, and Pearson correlation in terms of both relative bias and efficiency when moderate-to-heavy-tailed distributions are of concern.

Стилі APA, Harvard, Vancouver, ISO та ін.

29

Cotuk, Nilufen, Ahmet Hamdi Kayran, Alper Aytun, and Kubilay Savci. "Detection of Marine Noise Radars With Spectral Kurtosis Method." IEEE Aerospace and Electronic Systems Magazine 35, no. 9 (September 1, 2020): 22–31. http://dx.doi.org/10.1109/maes.2020.2990590.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

30

Spurek, Przemysław, Przemysław Rola, Jacek Tabor, Aleksander Czechowski, and Andrzej Bedychaj. "ICA based on Split Generalized Gaussian." Schedae Informaticae 28 (December 1, 2019): 25–47. http://dx.doi.org/10.4467/20838476si.19.002.14379.

Повний текст джерела

Анотація:

Independent Component Analysis (ICA) is a method for searching the linear transformation that minimizes the statistical dependence between its components. Most popular ICA methods use kurtosis as a metric of independence (non-Gaussianity) to maximize, such as FastICA and JADE. However, their assumption of fourth-order moment (kurtosis) may not always be satisfied in practice. One of the possible solution is to use third-order moment (skewness) instead of kurtosis, which was applied in ICA_SG and EcoICA. In this paper we present a competitive approach to ICA based on the Split Generalized Gaussian distribution (SGGD), which is well adapted to heavy-tailed as well as asymmetric data. Consequently, we obtain a method which works better than the classical approaches, in both cases: heavy tails and non-symmetric data

Стилі APA, Harvard, Vancouver, ISO та ін.

31

Pant, Mohan D., and Todd C. Headrick. "A Method for Simulating Burr Type III and Type XII Distributions through -Moments and -Correlations." ISRN Applied Mathematics 2013 (May 22, 2013): 1–14. http://dx.doi.org/10.1155/2013/191604.

Повний текст джерела

Анотація:

This paper derives the Burr Type III and Type XII family of distributions in the contexts of univariate -moments and the -correlations. Included is the development of a procedure for specifying nonnormal distributions with controlled degrees of -skew, -kurtosis, and -correlations. The procedure can be applied in a variety of settings such as statistical modeling (e.g., forestry, fracture roughness, life testing, operational risk, etc.) and Monte Carlo or simulation studies. Numerical examples are provided to demonstrate that -moment-based Burr distributions are superior to their conventional moment-based analogs in terms of estimation and distribution fitting. Evaluation of the proposed procedure also demonstrates that the estimates of -skew, -kurtosis, and -correlation are substantially superior to their conventional product moment-based counterparts of skew, kurtosis, and Pearson correlations in terms of relative bias and relative efficiency—most notably when heavy-tailed distributions are of concern.

Стилі APA, Harvard, Vancouver, ISO та ін.

32

Huang, Haifeng, Huajiang Ouyang, Hongli Gao, Liang Guo, Dan Li, and Juan Wen. "A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation." Measurement Science Review 16, no. 3 (June 1, 2016): 149–59. http://dx.doi.org/10.1515/msr-2016-0018.

Повний текст джерела

Анотація:

Abstract Detection of incipient degradation demands extracting sensitive features accurately when signal-to-noise ratio (SNR) is very poor, which appears in most industrial environments. Vibration signals of rolling bearings are widely used for bearing fault diagnosis. In this paper, we propose a feature extraction method that combines Blind Source Separation (BSS) and Spectral Kurtosis (SK) to separate independent noise sources. Normal, and incipient fault signals from vibration tests of rolling bearings are processed. We studied 16 groups of vibration signals (which all display an increase in kurtosis) of incipient degradation after they are processed by a BSS filter. Compared with conventional kurtosis, theoretical studies of SK trends show that the SK levels vary with frequencies and some experimental studies show that SK trends of measured vibration signals of bearings vary with the amount and level of impulses in both vibration and noise signals due to bearing faults. It is found that the peak values of SK increase when vibration signals of incipient faults are processed by a BSS filter. This pre-processing by a BSS filter makes SK more sensitive to impulses caused by performance degradation of bearings.

Стилі APA, Harvard, Vancouver, ISO та ін.

33

Nieniewski, Mariusz, and Paweł Zajączkowski. "COMPARISON OF ULTRASOUND IMAGE FILTERING METHODS BY MEANS OF MULTIVARIABLE KURTOSIS." Image Analysis & Stereology 36, no. 2 (June 23, 2017): 79. http://dx.doi.org/10.5566/ias.1639.

Повний текст джерела

Анотація:

Comparison of the quality of despeckled US medical images is complicated because there is no image of a human body that would be free of speckles and could serve as a reference. A number of various image metrics are currently used for comparison of filtering methods; however, they do not satisfactorily represent the visual quality of images and medical expert’s satisfaction with images. This paper proposes an innovative use of relative multivariate kurtosis for the evaluation of the most important edges in an image. Multivariate kurtosis allows one to introduce an order among the filtered images and can be used as one of the metrics for image quality evaluation. At present there is no method which would jointly consider individual metrics. Furthermore, these metrics are typically defined by comparing the noisy original and filtered images, which is incorrect since the noisy original cannot serve as a golden standard. In contrast to this, the proposed kurtosis is the absolute measure, which is calculated independently of any reference image and it agrees with the medical expert’s satisfaction to a large extent. The paper presents a numerical procedure for calculating kurtosis and describes results of such calculations for a computer-generated noisy image, images of a general purpose phantom and a cyst phantom, as well as real-life images of thyroid and carotid artery obtained with SonixTouch ultrasound machine. 16 different methods of image despeckling are compared via kurtosis. The paper shows that visually more satisfactory despeckling results are associated with higher kurtosis, and to a certain degree kurtosis can be used as a single metric for evaluation of image quality.

Стилі APA, Harvard, Vancouver, ISO та ін.

34

Li, Yunfeng, Liqin Wang, and Jian Guan. "A Spectrum Detection Approach for Bearing Fault Signal Based on Spectral Kurtosis." Shock and Vibration 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6106103.

Повний текст джерела

Анотація:

According to the similarity between Morlet wavelet and fault signal and the sensitive characteristics of spectral kurtosis for the impact signal, a new wavelet spectrum detection approach based on spectral kurtosis for bearing fault signal is proposed. This method decreased the band-pass filter range and reduced the wavelet window width significantly. As a consequence, the bearing fault signal was detected adaptively, and time-frequency characteristics of the fault signal can be extracted accurately. The validity of this method was verified by the identifications of simulated shock signal and test bearing fault signal. The method provides a new understanding of wavelet spectrum detection based on spectral kurtosis for rolling element bearing fault signal.

Стилі APA, Harvard, Vancouver, ISO та ін.

35

Sarker, Ronobir, Amandeep Kaur, and D. Singh. "Noise Estimation Using Back Propagation Neural Networks." ECS Transactions 107, no. 1 (April 24, 2022): 18761–68. http://dx.doi.org/10.1149/10701.18761ecst.

Повний текст джерела

Анотація:

In this paper, a new Backpropagation Neural Network-based noise estimation method is proposed to estimate Rician noise from MRI images. To train BNN features of MRI images such as contrast, homogeneity, dissimilarity, asm, energy, entropy, mean x, mean y, mean glcm, var x, var y, var glcm, correlation, skew x, skew y, skew, kurtosis x, kurtosis y, kurtosis, etc. are used. For training BNN, 450 images are used which are downloaded from BrainWeb.

Стилі APA, Harvard, Vancouver, ISO та ін.

36

Qin, Bo, Zixian Li, and Yan Qin. "A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes." Strojniški vestnik – Journal of Mechanical Engineering 66, no. 6 (June 15, 2020): 385–94. http://dx.doi.org/10.5545/sv-jme.2020.6546.

Повний текст джерела

Анотація:

Sensitive and accurate fault features from the vibration signals of planetary gearboxes are essential for fault diagnosis, in which extreme learning machine (ELM) techniques have been widely adopted. To increase the sensitivity of extracted features fed in ELM, a novel feature extraction method is put forward, which takes advantage of the transient dynamics and the reconstructed high-dimensional data from the original vibration signal. First, based on fast kurtosis analysis, the range of transient dynamics of a vibration signal is located. Next, with the extracted kurtosis information, with variational mode decomposition, a series of intrinsic mode functions are decomposed; the ones that fall into the obtained ranges are selected as transient features, corresponding to maximum kurtosis value. Fed by the transient features, a hierarchical ELM model is well-trained for fault classification. Furthermore, a denoising auto-encoder is used to optimize input weight and threshold of implicit learning node of ELM, satisfying orthogonal condition to realize the layering of its hidden layers. Finally, a numerical case and an experiment are conducted to verify the performance of the proposed method. In comparison with its counterparts, the proposed method has a better classification accuracy in the aiding of transient features.

Стилі APA, Harvard, Vancouver, ISO та ін.

37

Chen, Long, Yat Sze Choy, Tian Gang Wang, and Yan Kei Chiang. "Fault detection of wheel in wheel/rail system using kurtosis beamforming method." Structural Health Monitoring 19, no. 2 (June 14, 2019): 495–509. http://dx.doi.org/10.1177/1475921719855444.

Повний текст джерела

Анотація:

Fault detection systems are typically applied in the railway industry to examine the structural health status of the wheel/rail system. We herein propose a time-domain kurtosis beamforming technique using an array of microphones for the fault identification and localisation of the wheel/rail system under an environment with high background noise. As an acoustics-based noncontact diagnosis method, this technique overcomes the challenge of the contact between the sensors and examined structures, and it is more applicable for impulsive signals of broadband nature, such as impact noise generated from faults on the wheel surface. Moreover, the application of kurtosis enables the identification and localisation at low signal-to-noise ratio. Under such circumstance, the impulsive signals generated by faults were totally merged in rolling noise and background noise. Meanwhile, different types of faults on the wheels could be identified and localised by observing the kurtosis value on the beamforming sound map. The effectiveness of the proposed method to diagnose the type of wheel fault with low signal-to-noise ratio and moving source has been validated experimentally. This method may provide a useful tool for the routine maintenance of trains.

Стилі APA, Harvard, Vancouver, ISO та ін.

38

Zhang, Zhen, Zhe Ming Duan, and Ying Long. "A Study of Kurtosis-Based Dictionary-Diagnosis Method against Switch-Current Circuit Faults." Applied Mechanics and Materials 705 (December 2014): 199–203. http://dx.doi.org/10.4028/www.scientific.net/amm.705.199.

Повний текст джерела

Анотація:

For the purpose of solving the problem of diagnosing the Switched current circuits’ fault,a location algorithm based on information kurtosis was proposed.Data acquisition board or ASI-Z software was used to gather the data of the fault circuit output terminal.Then information Kurtosis of the Switched current circuit time domain response was calculated.Finally fault dictionary was built to classify faults.The location algorithm was simulated in a practical circuit.The results show that the location algorithm is use to simplify the structure of fault dictionary,and classify the faults,and it is applicable to diagnosising switch current circuit fault.

Стилі APA, Harvard, Vancouver, ISO та ін.

39

Liu, Yanfang, Shiqiang Li, and Heng Zhang. "Multibaseline Interferometric Phase Denoising Based on Kurtosis in the NSST Domain." Sensors 20, no. 2 (January 19, 2020): 551. http://dx.doi.org/10.3390/s20020551.

Повний текст джерела

Анотація:

Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the baseline-invariant property of kurtosis in this paper. Some numerical experiments and rational analyses confirm its validity and universality. The noise level estimation of nature images is extended to multibaseline InSAR by dint of the baseline-invariant property of kurtosis. A filtering method based on the non-subsampled shearlet transform (NSST) and Wiener filter with estimated noise variance is proposed then. Firstly, multi-scaled and multi-directional coefficients of interferograms are obtained by NSST. Secondly, the noise variance is represented as the solution of a constrained non-convex optimization problem. A pre-thresholded Wiener filtering with estimated noise variance is employed for shrinking or zeroing NSST coefficients. Finally, the inverse NSST is utilized to obtain the filtered interferograms. Experiments on simulated and real data show that the proposed method has excellent comprehensive performance and is superior to conventional single-baseline filtering methods.

Стилі APA, Harvard, Vancouver, ISO та ін.

40

Lu, Jiantao, Wei Cheng, Yapeng Chu, and Yanyang Zi. "Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems." Journal of Vibration and Control 25, no. 16 (June 12, 2019): 2246–60. http://dx.doi.org/10.1177/1077546319852483.

Повний текст джерела

Анотація:

To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO). First, an improved contrast function is presented by combining mutual information of the separated signals and kurtosis ranges of source signals. Second, an augmented Lagrangian multiplier method is used to convert PNLBSS into an unconstrained pseudo-objective optimization problem. Then, improved PSO is applied to update the parameters in complex nonlinear spaces. Finally, numerical case studies and experimental case studies are provided to evaluate the performance of the proposed method. By adding the kurtosis ranges constraints, the estimation accuracy of source signals could be improved, which would benefit vibration and acoustic monitoring and control.

Стилі APA, Harvard, Vancouver, ISO та ін.

41

Jiang, Fei, Honglang Li, Zhenhai Zhang, Yixin Zhang, and Xuping Zhang. "Localization and Discrimination of the Perturbation Signals in Fiber Distributed Acoustic Sensing Systems Using Spatial Average Kurtosis." Sensors 18, no. 9 (August 28, 2018): 2839. http://dx.doi.org/10.3390/s18092839.

Повний текст джерела

Анотація:

Location error and false alarm are noticeable problems in fiber distributed acoustic sensing systems based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). A novel method based on signal kurtosis is proposed to locate and discriminate perturbations in Φ-OTDR systems. The spatial kurtosis (SK) along the fiber is firstly obtained by calculating the kurtosis of acoustic signals at each position of the fiber in a short time period. After the moving average on the spatial dimension, the spatial average kurtosis (SAK) is then obtained, whose peak can accurately locate the center of the vibration segment. By comparing the SAK value with a certain threshold, we may to some degree discriminate the instantaneous destructive perturbations from the system noise and certain ambient environmental interferences. The experimental results show that, comparing with the average of the previous localization methods, the SAK method improves the pencil-break and digging locating signal-to-noise ratio (SNR) by 16.6 dB and 17.3 dB, respectively; and decreases the location standard deviation by 7.3 m and 9.1 m, respectively. For the instantaneous destructive perturbation (pencil-break and digging) detection, the false alarm rate can be as low as 1.02%, while the detection probability is maintained as high as 95.57%. In addition, the time consumption of the SAK method is adequate for a real-time Φ-OTDR system.

Стилі APA, Harvard, Vancouver, ISO та ін.

42

Gao, Wei, and Huai-Shan Liu. "Strong noise attenuation method based on the multiuser kurtosis criterion." Applied Geophysics 10, no. 1 (March 2013): 25–32. http://dx.doi.org/10.1007/s11770-013-0365-5.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

43

Zhao, Yong Jian, Mei Xia Qu, and Hai Ning Jiang. "A Method for Extracting Signals with Specific Normalized Kurtosis Range." Advanced Materials Research 756-759 (September 2013): 3845–48. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3845.

Повний текст джерела

Анотація:

The famous FastICA algorithm has been widely used for blind signal separation. For every process, it only converges to an original source which has the maximum negentropy of the underlying signals. To ensure the first output is the desired signal, we incorporate a priori knowledge as a constraint into the FastICA algorithm to construct a robust blind source extraction algorithm. One can extract the desired signal if its normalized kurtosis is known to lie in a specific range, whereas other unwanted signals do not belong to this range. Experimental results on biomedical signals illustrate the validity and reliability of the proposed method.

Стилі APA, Harvard, Vancouver, ISO та ін.

44

Yesilyurt, Murat, Yildiray Yalman, and A. Turan Ozcerit. "A Robust Watermarking Method for Mpeg-4 Based on Kurtosis." Computer Journal 58, no. 7 (October 24, 2014): 1645–55. http://dx.doi.org/10.1093/comjnl/bxu112.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

45

Headrick, Todd C., and Shlomo S. Sawilowsky. "Weighted Simplex Procedures for Determining Boundary Points and Constants for the Univariate and Multivariate Power Methods." Journal of Educational and Behavioral Statistics 25, no. 4 (December 2000): 417–36. http://dx.doi.org/10.3102/10769986025004417.

Повний текст джерела

Анотація:

The power methods are simple and efficient algorithms used to generate either univariate or multivariate nonnormal distributions with specified values of (marginal) mean, standard deviation, skew, and kurtosis. The power methods are bounded as are other transformation techniques. Given an exogenous value of skew, there is an associated lower bound of kurtosis. Previous approximations of the boundary for the power methods are either incorrect or inadequate. Data sets from education and psychology can be found to lie within, near, or outside tile boundary of the power methods. In view of this, we derived necessary and sufficient conditions using the Lagrange multiplier method to determine the boundary of the power methods. The conditions for locating and classifying modes for distributions on the boundary were also derived. Self-contained interactive Fortran programs using a Weighted Simplex Procedure were employed to generate tabled values of minimum kurtosis for a given value of skew and power constants for various (non)normal distributions.

Стилі APA, Harvard, Vancouver, ISO та ін.

46

Liu, Yuhu, Yi Chai, Bowen Liu, and Yiming Wang. "Impulse Signal Detection for Bearing Fault Diagnosis via Residual-Variational Mode Decomposition." Applied Sciences 11, no. 7 (March 29, 2021): 3053. http://dx.doi.org/10.3390/app11073053.

Повний текст джерела

Анотація:

A novel method named residual-variational mode decomposition (RVMD) is proposed in this study to extract bearing fault features accurately. RVMD can determine the number of modes and the balance parameter adaptively, and it has two stages. In the first stage, the signal is decomposed into a series of modes until the correlation coefficient between the raw signal and the decomposition results reaches the threshold. A redefined kurtosis, which can resist the interferences from aperiodic impulse efficiency, is applied to rebuild the ensemble kurtosis index. The mode that has the largest rebuild-ensemble kurtosis, and its neighbors, are kept. By putting the residual signal into the second stage, an iteration process is applied to determine the optimal parameters for variational mode decomposition (VMD). VMD is re-run with the optimal parameters, and the sub-mode filtered with the larger rebuild-ensemble kurtosis is examined by the envelope analysis technology to observe the fault feature. The effectiveness of RVMD is verified by the simulation signal and three experiment signals. Its superiority is shown by comparing it with some existing methods.

Стилі APA, Harvard, Vancouver, ISO та ін.

47

Zhang, Yong Xiang, Jie Ping Zhu, and Shuai Zhang. "Simulation Research on Rolling Element Bearing Fault Signal Extraction Based on Blind Source Separation." Advanced Materials Research 989-994 (July 2014): 3738–42. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3738.

Повний текст джерела

Анотація:

In order to extract the fault information from rolling element bearing, combined with Kurtosis criteria and Hessian matrix. An improved rolling element bearing fault signal extraction method is proposed. Kurtosis is the cost function. The method is according to the construction principles of blind source separation (BSS), and it uses an analytically derived Hessian matrix in the maximization process of the cost function used. Then the impact signal is extracted successfully. The effectiveness of the method is demonstrated on simulated signal.

Стилі APA, Harvard, Vancouver, ISO та ін.

48

Wang, Xiao Lin, Yong Xiang Zhang, Jie Ping Zhu, and Zhong Qi Shi. "Research on Rolling Element Bearing Fault Diagnosis Based on Singular Value Decomposition and Kurtosis Criterion." Applied Mechanics and Materials 432 (September 2013): 304–9. http://dx.doi.org/10.4028/www.scientific.net/amm.432.304.

Повний текст джерела

Анотація:

In order to extract the faint fault information from complicated vibration signal of bearing, a new feature extraction method based on singular value decomposition (SVD) and kurtosis criterion is proposed in my work. According to the method, a group of component signals are obtained firstly using SVD, then component signals with equal kurtosis are selected to be summed together, and the weak fault signal is clearly extracted. The effectiveness of the method is demonstrated on both simulated signal and actual data.

Стилі APA, Harvard, Vancouver, ISO та ін.

49

Zheng, Ronghui, Huaihai Chen, Min Qin, Andrea Angeli, and Dirk Vandepitte. "Analysis of low damping ratios in multi-exciter stationary non-Gaussian random vibration control." Journal of Vibration and Control 26, no. 17-18 (January 6, 2020): 1463–70. http://dx.doi.org/10.1177/1077546319898561.

Повний текст джерела

Анотація:

This article investigates the influence of low damping ratios on the performance of the multi-exciter stationary non-Gaussian random vibration control system. The basic theory of the multi-exciter stationary non-Gaussian random vibration method is reviewed first, and then the influences of low damping ratios on multi-output spectra and kurtoses are analyzed. The low damping ratios cause an ill-conditioned problem which will make the drive spectral matrix solution inaccurate; thus, some spectral lines located at resonance peaks in the response spectra cannot be modified within the preset tolerances by the control algorithms. The regularization method is used to alleviate the calculation error. The output kurtoses are dependent not only on the characteristics of the system but also on the input signals. It is found that the kurtosis control will be intractable if the damping ratios are very low. A two-input two-output cantilever beam simulation example is described to illustrate the analysis results.

Стилі APA, Harvard, Vancouver, ISO та ін.

50

Wang, Heng-di, Si-er Deng, Jian-xi Yang, Hui Liao, and Wen-bo Li. "Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis." Mathematical Problems in Engineering 2020 (February 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/5659618.

Повний текст джерела

Анотація:

In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

Стилі APA, Harvard, Vancouver, ISO та ін.

Статті в журналах з теми "Kurtosis method"

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями