It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent...It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.展开更多
Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of ...Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.展开更多
Electroencephalogram(EEG) signal preprocessing is one of the most important techniques in brain computer interface(BCI).The target is to increase signal-to-noise ratio and make it more favorable for feature extraction...Electroencephalogram(EEG) signal preprocessing is one of the most important techniques in brain computer interface(BCI).The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition.Wavelet transform is a method of multi-resolution time-frequency analysis,it can decompose the mixed signals which consist of different frequencies into different frequency band.EEG signal is analyzed and denoised using wavelet transform.Moreover,wavelet transform can be used for EEG feature extraction.The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features.The eigenvector for classification is obtained by combining the effective features from different channels.The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition,the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.展开更多
Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extrac...Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.展开更多
This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the ...This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.展开更多
With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applica...With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.展开更多
This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prer...This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prerequisite is to detect the corners of pulse signals correctly. Up to now,the pulse parameters are mostly acquired by marking the pulse corners manually,which is an obstacle to modernize pulse diagnosis. Therefore,a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately,which can be expected to facilitate the modernization of pulse diagnosis.展开更多
Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise ge...Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise generated from harsh operations and instruments,while the local feature pattern and long-term dependency in the wastewater quality time series,the prediction performance can be degraded.In this paper,a discrete wavelet transform and convolutional enhanced Transformer(DWT-Ce Transformer) method is developed to predict the influent quality in WWTPs.Specifically,we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform,effectively removing noise while preserving key data characteristics.Further,a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features,and then these local features are combined with Transformer's self-attention mechanism,so that the model can not only capture long-term dependencies,but also retain the sensitivity to local context.In this study,we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2.The experimental results show that,compared to baseline models,DWT-Ce Transformer can significantly improve the prediction performance of influent COD and NH_(3)-N.Specifically,MSE,MAE,and RMSE improve by 78.7%,79.5%,and 53.8% for COD,and 79.4%,70.2%,and 54.5% for NH_(3)-N.On simulated data,our method shows strong improvements under various weather conditions,especially in dry weather,with MSE,MAE,and RMSE for COD improving by 68.9%,48.0%,and 44.3%,and for NH_(3)-N by 78.4%,54.8%,and 53.2%.展开更多
A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification...A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.展开更多
The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective...The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection methods.This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases.The proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process.The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.展开更多
An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimens...An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.展开更多
In order to extract the fault feature frequency of weak bearing signals,we put forward a local mean decomposition(LMD)method combining with the second generation wavelet transform.After performing the second generatio...In order to extract the fault feature frequency of weak bearing signals,we put forward a local mean decomposition(LMD)method combining with the second generation wavelet transform.After performing the second generation wavelet denoising,the spline-based LMD is used to decompose the high-frequency detail signals of the second generation wavelet signals into a number of production functions(PFs).Power spectrum analysis is applied to the PFs to detect bearing fault information and identify the fault patterns.Application in inner and outer race fault diagnosis of rolling bearing shows that the method can extract the vibration features of rolling bearing fault.This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.展开更多
A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method ...A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method provides a finer analysis and better time-frequencylocalization capabilities than any other analysis methods. Both details and approximations are splitinto finer components and result in better-localized frequency ranges corresponding to each node ofa wavelet packet tree. For the punpose of feature extraction, a hard threshold is given and theenergy of the coefficients above the threshold is used, as a criterion for the selection of the bestvector. The feature extraction of a vibration signal is accomplished by computing thereconstruction signal and its spectrum. When applied to a rolling bear vibration signal featureextraction, the proposed method can lead to be very effective.展开更多
Currently, accurately extracting early-stage bearing incipient fault features is urgent and challenging. This paper introduces a novel method called adaptive multiscale wavelet-guided periodic sparse representation(AM...Currently, accurately extracting early-stage bearing incipient fault features is urgent and challenging. This paper introduces a novel method called adaptive multiscale wavelet-guided periodic sparse representation(AMWPSR) to address this issue. For the first time, the dual-tree complex wavelet transform is applied to construct the linear transformation for the AMWPSR model.This transform offers superior shift invariance and minimizes spectrum aliasing. By integrating this linear transformation with the generalized minimax concave penalty term, a new sparse representation model is developed to recover faulty impulse components from heavily disturbed vibration signals. During each iteration of the AMWPSR process, the impulse periods of sparse signals are adaptively estimated, and the periodicity of the latest sparse signal is augmented using the final estimated period. Simulation studies demonstrate that AMWPSR can effectively estimate periodic impulses even in noisy environments, demonstrating greater accuracy and robustness in recovering faulty impulse components than existing techniques.Further validation through research on two sets of bearing life cycle data shows that AMWPSR delivers superior fault diagnosis results.展开更多
Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recog...Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recognition of cracks is essential because the surface of hot slabs is very complicated. In order to detect the surface longitudinal cracks of the slabs, a new feature extraction method based on Curvelet transform and kernel locality preserving projections （KLPP） is proposed. First, sample images are decomposed into three levels by Curvelet transform. Second, Fourier transform is applied to all sub-band images and the Fourier amplitude spectrum of each sub-band is computed to get features with translational invariance. Third, five kinds of statistical features of the Fourier amplitude spectrum are computed and combined in different forms. Then, KLPP is employed for dimensionality reduction of the obtained 62 types of high-dimensional combined features. Finally, a support vector machine （SVM） is used for sample set classification. Experiments with samples from a real production line of continuous casting slabs show that the algorithm is effective to detect longitudinal cracks, and the classification rate is 91.89%.展开更多
Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (...Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.展开更多
基金This project is supported by National Natural Science Foundation of China (No.50275154) Municipal Natural Science Foundation of Chongqing, China (No.8773).
文摘It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.
基金the supported by National Natural Science Foundation of China(No.61871318 and 11574250)Scientific Research Plan Projects of Shaanxi Education Department(No.19JK0568).
文摘Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.
文摘Electroencephalogram(EEG) signal preprocessing is one of the most important techniques in brain computer interface(BCI).The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition.Wavelet transform is a method of multi-resolution time-frequency analysis,it can decompose the mixed signals which consist of different frequencies into different frequency band.EEG signal is analyzed and denoised using wavelet transform.Moreover,wavelet transform can be used for EEG feature extraction.The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features.The eigenvector for classification is obtained by combining the effective features from different channels.The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition,the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.
基金supported financially by FundamentalResearch Program of Shanxi Province(No.202103021223056).
文摘Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.
文摘This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.
文摘With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.
文摘This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prerequisite is to detect the corners of pulse signals correctly. Up to now,the pulse parameters are mostly acquired by marking the pulse corners manually,which is an obstacle to modernize pulse diagnosis. Therefore,a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately,which can be expected to facilitate the modernization of pulse diagnosis.
基金funded by the Natural Science Basic Research Program of Shaanxi (2024JCYBMS576)the National Natural Science Foundation of China (62366053)。
文摘Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise generated from harsh operations and instruments,while the local feature pattern and long-term dependency in the wastewater quality time series,the prediction performance can be degraded.In this paper,a discrete wavelet transform and convolutional enhanced Transformer(DWT-Ce Transformer) method is developed to predict the influent quality in WWTPs.Specifically,we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform,effectively removing noise while preserving key data characteristics.Further,a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features,and then these local features are combined with Transformer's self-attention mechanism,so that the model can not only capture long-term dependencies,but also retain the sensitivity to local context.In this study,we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2.The experimental results show that,compared to baseline models,DWT-Ce Transformer can significantly improve the prediction performance of influent COD and NH_(3)-N.Specifically,MSE,MAE,and RMSE improve by 78.7%,79.5%,and 53.8% for COD,and 79.4%,70.2%,and 54.5% for NH_(3)-N.On simulated data,our method shows strong improvements under various weather conditions,especially in dry weather,with MSE,MAE,and RMSE for COD improving by 68.9%,48.0%,and 44.3%,and for NH_(3)-N by 78.4%,54.8%,and 53.2%.
文摘A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.
文摘The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection methods.This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases.The proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process.The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.
文摘An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.
基金the Key Fund Project of Sichuan Provincial Department of Education(No.13CZ0012)
文摘In order to extract the fault feature frequency of weak bearing signals,we put forward a local mean decomposition(LMD)method combining with the second generation wavelet transform.After performing the second generation wavelet denoising,the spline-based LMD is used to decompose the high-frequency detail signals of the second generation wavelet signals into a number of production functions(PFs).Power spectrum analysis is applied to the PFs to detect bearing fault information and identify the fault patterns.Application in inner and outer race fault diagnosis of rolling bearing shows that the method can extract the vibration features of rolling bearing fault.This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.
文摘A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method provides a finer analysis and better time-frequencylocalization capabilities than any other analysis methods. Both details and approximations are splitinto finer components and result in better-localized frequency ranges corresponding to each node ofa wavelet packet tree. For the punpose of feature extraction, a hard threshold is given and theenergy of the coefficients above the threshold is used, as a criterion for the selection of the bestvector. The feature extraction of a vibration signal is accomplished by computing thereconstruction signal and its spectrum. When applied to a rolling bear vibration signal featureextraction, the proposed method can lead to be very effective.
基金supported by the National Natural Science Foundation of China (Grant No. 51875459)。
文摘Currently, accurately extracting early-stage bearing incipient fault features is urgent and challenging. This paper introduces a novel method called adaptive multiscale wavelet-guided periodic sparse representation(AMWPSR) to address this issue. For the first time, the dual-tree complex wavelet transform is applied to construct the linear transformation for the AMWPSR model.This transform offers superior shift invariance and minimizes spectrum aliasing. By integrating this linear transformation with the generalized minimax concave penalty term, a new sparse representation model is developed to recover faulty impulse components from heavily disturbed vibration signals. During each iteration of the AMWPSR process, the impulse periods of sparse signals are adaptively estimated, and the periodicity of the latest sparse signal is augmented using the final estimated period. Simulation studies demonstrate that AMWPSR can effectively estimate periodic impulses even in noisy environments, demonstrating greater accuracy and robustness in recovering faulty impulse components than existing techniques.Further validation through research on two sets of bearing life cycle data shows that AMWPSR delivers superior fault diagnosis results.
基金Sponsored by Program for New Century Excellent Talents in University of China(NCET-08-0726)Beijing Nova Program of China(2007B027)
文摘Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recognition of cracks is essential because the surface of hot slabs is very complicated. In order to detect the surface longitudinal cracks of the slabs, a new feature extraction method based on Curvelet transform and kernel locality preserving projections （KLPP） is proposed. First, sample images are decomposed into three levels by Curvelet transform. Second, Fourier transform is applied to all sub-band images and the Fourier amplitude spectrum of each sub-band is computed to get features with translational invariance. Third, five kinds of statistical features of the Fourier amplitude spectrum are computed and combined in different forms. Then, KLPP is employed for dimensionality reduction of the obtained 62 types of high-dimensional combined features. Finally, a support vector machine （SVM） is used for sample set classification. Experiments with samples from a real production line of continuous casting slabs show that the algorithm is effective to detect longitudinal cracks, and the classification rate is 91.89%.
文摘Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.
