Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displ...Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.展开更多
Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the predi...Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.展开更多
Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(E...In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.展开更多
The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for e...The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.展开更多
Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detecti...Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.展开更多
In order to improve the accuracy of wind turbine fault diagnosis,a wind turbine fault diagnosis method based on Subtraction-Average-Based Optimizer(SABO)optimized Variational Mode Decomposition(VMD)and Kernel Extreme ...In order to improve the accuracy of wind turbine fault diagnosis,a wind turbine fault diagnosis method based on Subtraction-Average-Based Optimizer(SABO)optimized Variational Mode Decomposition(VMD)and Kernel Extreme Learning Machine(KELM)is proposed.Firstly,the SABO algorithm was used to optimize the VMD parameters and decompose the original signal to obtain the best modal components,and then the nine features were calculated to obtain the feature vectors.Secondly,the SABO algorithm was used to optimize the KELM parameters,and the training set and the test set were divided according to different proportions.The results were compared with the optimized model without SABO algorithm.The experimental results show that the fault diagnosis method of wind turbine based on SABO-VMD-KELM model can achieve fault diagnosis quickly and effectively,and has higher accuracy.展开更多
Radio frequency fingerprinting(RFF)is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level,device-specific imperfections.The RF...Radio frequency fingerprinting(RFF)is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level,device-specific imperfections.The RFF-related information is mainly in the form of unintentional modulation(UIM),which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation(IM).It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF.This paper proposes a UIM microstructure enlargement(UMME)method based on feature-level adaptive signal decomposition(ASD),accompanied by autocorrelation and cross-correlation analysis.The common IM part is evaluated by analyzing a newly-defined benchmark feature.Three different indexes are used to quantify the similarity,distance,and dependency of the RFF features from different devices.Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode.The visual image qualitatively shows the magnification of feature differences;different indicators quantitatively describe the changes in features.Compared with the original RFF feature,recognition results based on the Gaussian mixture model(GMM)classifier further validate the effectiveness of the proposed algorithm.展开更多
In the pump-controlled motor hydraulic transmission system,when the pressure pulsation frequencies seperately generated by the pump and the motor are close to each other,the hydraulic system will generate a strong pre...In the pump-controlled motor hydraulic transmission system,when the pressure pulsation frequencies seperately generated by the pump and the motor are close to each other,the hydraulic system will generate a strong pressure beat vibration phenomenon,which will seriously affect the smooth running of the hydraulic system.However,the modulated pressure signal also carries information related to the operating state of the hydraulic system,and a accurate extraction of pressure vibration characteristics is the key to obtain the operating state information of the hydraulic system.In order to extract the pressure beat vibration signal component effectively from the multi-component time-varying aliasing pressure signal and reconstruct the time domain characteristics,an extraction method of the pressure beat vibration characteristics of the hydraulic transmission system based on variational mode decomposition(VMD)is proposed.The experimental results show that the VMD method can accurately extract the pressure beat vibration characteristics from the high-pressure oil pressure signal of the hydraulic system,and the extraction effect is preferable to that of the traditional signal processing methods such as empirical mode decomposition(EMD).展开更多
The axial piston pump usually works under variable speed conditions.It is important to evaluate the health status of the axial piston pump under the variable speed condition.Aiming at the characteristic signals obtain...The axial piston pump usually works under variable speed conditions.It is important to evaluate the health status of the axial piston pump under the variable speed condition.Aiming at the characteristic signals obtained under different wear levels of the port plate,a feature signal extraction method under variable speed conditions is proposed.Firstly,the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)energy spectrum and fast spectral kurtosis principle is used to accurately extract the intrinsic mode function(IMF)component containing the sensitive information of the degraded feature.Then,the aspect ratio analysis method of the angle domain variational mode decomposition(VMD)is used to process the feature index containing the sensitive information of the degraded feature.In order to evaluate the health status of the axial piston pump under variable speed,the vibration reliability analysis method for axial piston pump based on Weibull proportional failure rate model is proposed.The experimental results show that the proposed method can accurately evaluate the health status of the axial piston pump.展开更多
Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty,multi-frequency and nonlinearity for it is su...Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty,multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed.Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection.Firstly, the variational mode decomposition(VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy(m RMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network(BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine(LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.展开更多
Abundant system operation state information is included in the electrical signal of the hydraulic system motor.How to accurately extract and classify the operation information of electrical signal is the key to realiz...Abundant system operation state information is included in the electrical signal of the hydraulic system motor.How to accurately extract and classify the operation information of electrical signal is the key to realize the condition monitoring of hydraulic system.The early fault characteristics of hydraulic gear pump hidden in the motor current signal are weak and difficult to extract by traditional time-frequency analysis.Based on the correlation coefficient and artificial bee colony algorithm(ABC),the parameter optimization of variational mode decomposition(VMD)is realized in this paper.At the same time,the principle of maximum signal correlation coefficient and kurtosis value is adopted to determine the effective intrinsic mode function(IMF).Moreover,the permutation entropy(PE)and root mean square(RMS)of the effective IMF components are input into the deep belief network(DBN-DNN)as high-dimensional feature vectors.The operation state of gear pump is monitored.The results show that the weak characteristics of current signal of gear pump fault are accurately and stably extracted by this method.The running state of gear pump is monitored and the accuracy of gear fault diagnosis is improved.展开更多
The ultrasonic echo in liquid density measurement often suffers noise,which makes it difficult to obtain the useful echo waveform,resulting in low accuracy of density measurement.A denoising method based on improved v...The ultrasonic echo in liquid density measurement often suffers noise,which makes it difficult to obtain the useful echo waveform,resulting in low accuracy of density measurement.A denoising method based on improved variational mode decomposition(VMD)for noise echo signals is proposed.The number of decomposition layers of the traditional VMD is hard to determine,therefore,the center frequency similarity factor is firstly constructed and used as the judgment criterion to select the number of VMD decomposition layers adaptively;Secondly,VMD algorithm is used to decompose the echo signal into several modal components with a single modal component,and the useful echo components are extracted based on the features of the ultrasonic emission signal;Finally,the liquid density is calculated by extracting the amplitude and time of the echo from the modal components.The simulation results show that using the improved VMD to decompose the echo signal not only can improve the signal-to-noise ratio of the echo signal to 20.64 dB,but also can accurately obtain the echo information such as time and amplitude.Compared with the ensemble empirical mode decomposition(EEMD),this method effectively suppresses the modal aliasing,keeps the details of the signal to the maximum extent while suppressing noise,and improves the accuracy of the liquid density measurement.The density measurement accuracy can reach 0.21%of full scale.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51674169)Department of Education of Hebei Province of China(Grant No.ZD2019140)+1 种基金Natural Science Foundation of Hebei Province of China(Grant No.F2019210243)S&T Program of Hebei(Grant No.22375413D)School of Electrical and Electronics Engineering。
文摘Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.
基金supported by National Natural Science Foundation of China(Nos.61662042,62062049)Science and Technology Plan of Gansu Province(Nos.21JR7RA288,21JR7RE174).
文摘Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
基金The National Natural Science Foundation of Chin(No.51975117)
文摘In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.
基金the Institute of Noise and Vibration UTM for funding the study under the Higher Institution Centre of Excellence(HICoE)Grant Scheme (No.R.K130000.7809. 4J226)Additional funding for this research also comes from the UTM Research University Grant (No.Q. K130000.2543.11H36)Fundamental Research Grant Scheme(No.R.K130000.7840.4F653)by the Ministry of Higher Education Malaysia
文摘The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.
文摘Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.
文摘In order to improve the accuracy of wind turbine fault diagnosis,a wind turbine fault diagnosis method based on Subtraction-Average-Based Optimizer(SABO)optimized Variational Mode Decomposition(VMD)and Kernel Extreme Learning Machine(KELM)is proposed.Firstly,the SABO algorithm was used to optimize the VMD parameters and decompose the original signal to obtain the best modal components,and then the nine features were calculated to obtain the feature vectors.Secondly,the SABO algorithm was used to optimize the KELM parameters,and the training set and the test set were divided according to different proportions.The results were compared with the optimized model without SABO algorithm.The experimental results show that the fault diagnosis method of wind turbine based on SABO-VMD-KELM model can achieve fault diagnosis quickly and effectively,and has higher accuracy.
基金This work was supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China(2019JJ10004).
文摘Radio frequency fingerprinting(RFF)is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level,device-specific imperfections.The RFF-related information is mainly in the form of unintentional modulation(UIM),which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation(IM).It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF.This paper proposes a UIM microstructure enlargement(UMME)method based on feature-level adaptive signal decomposition(ASD),accompanied by autocorrelation and cross-correlation analysis.The common IM part is evaluated by analyzing a newly-defined benchmark feature.Three different indexes are used to quantify the similarity,distance,and dependency of the RFF features from different devices.Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode.The visual image qualitatively shows the magnification of feature differences;different indicators quantitatively describe the changes in features.Compared with the original RFF feature,recognition results based on the Gaussian mixture model(GMM)classifier further validate the effectiveness of the proposed algorithm.
基金National Natural Science Foundation of China(No.51675399)。
文摘In the pump-controlled motor hydraulic transmission system,when the pressure pulsation frequencies seperately generated by the pump and the motor are close to each other,the hydraulic system will generate a strong pressure beat vibration phenomenon,which will seriously affect the smooth running of the hydraulic system.However,the modulated pressure signal also carries information related to the operating state of the hydraulic system,and a accurate extraction of pressure vibration characteristics is the key to obtain the operating state information of the hydraulic system.In order to extract the pressure beat vibration signal component effectively from the multi-component time-varying aliasing pressure signal and reconstruct the time domain characteristics,an extraction method of the pressure beat vibration characteristics of the hydraulic transmission system based on variational mode decomposition(VMD)is proposed.The experimental results show that the VMD method can accurately extract the pressure beat vibration characteristics from the high-pressure oil pressure signal of the hydraulic system,and the extraction effect is preferable to that of the traditional signal processing methods such as empirical mode decomposition(EMD).
基金Supported by the National Key Research and Development Program of China(No.2019YFB2005204)the National Natural Science Foundation of China(No.52075469,51675461,11673040)+1 种基金the Key Research and Development Program of Hebei Province(No.19273708D)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(No.GZKF-201922).
文摘The axial piston pump usually works under variable speed conditions.It is important to evaluate the health status of the axial piston pump under the variable speed condition.Aiming at the characteristic signals obtained under different wear levels of the port plate,a feature signal extraction method under variable speed conditions is proposed.Firstly,the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)energy spectrum and fast spectral kurtosis principle is used to accurately extract the intrinsic mode function(IMF)component containing the sensitive information of the degraded feature.Then,the aspect ratio analysis method of the angle domain variational mode decomposition(VMD)is used to process the feature index containing the sensitive information of the degraded feature.In order to evaluate the health status of the axial piston pump under variable speed,the vibration reliability analysis method for axial piston pump based on Weibull proportional failure rate model is proposed.The experimental results show that the proposed method can accurately evaluate the health status of the axial piston pump.
基金supported by the National Key Research and Development Program of China (No. 2016YFC0401409)the Research Fund of the State Key Laboratory of Eco-hydraulics in Northwest Arid Region,Xi’ an University of Technology (No. 2019KJCXTD-5)+1 种基金the Key Research and Development Plan of Shaanxi Province (No. 2018-ZDCXL-GY-10-04)the Natural Science Basic Research Program of Shaanxi (No. 2019JLZ-15)。
文摘Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty,multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed.Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection.Firstly, the variational mode decomposition(VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy(m RMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network(BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine(LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.
基金National Natural Science Foundation of China(No.51675399)。
文摘Abundant system operation state information is included in the electrical signal of the hydraulic system motor.How to accurately extract and classify the operation information of electrical signal is the key to realize the condition monitoring of hydraulic system.The early fault characteristics of hydraulic gear pump hidden in the motor current signal are weak and difficult to extract by traditional time-frequency analysis.Based on the correlation coefficient and artificial bee colony algorithm(ABC),the parameter optimization of variational mode decomposition(VMD)is realized in this paper.At the same time,the principle of maximum signal correlation coefficient and kurtosis value is adopted to determine the effective intrinsic mode function(IMF).Moreover,the permutation entropy(PE)and root mean square(RMS)of the effective IMF components are input into the deep belief network(DBN-DNN)as high-dimensional feature vectors.The operation state of gear pump is monitored.The results show that the weak characteristics of current signal of gear pump fault are accurately and stably extracted by this method.The running state of gear pump is monitored and the accuracy of gear fault diagnosis is improved.
文摘The ultrasonic echo in liquid density measurement often suffers noise,which makes it difficult to obtain the useful echo waveform,resulting in low accuracy of density measurement.A denoising method based on improved variational mode decomposition(VMD)for noise echo signals is proposed.The number of decomposition layers of the traditional VMD is hard to determine,therefore,the center frequency similarity factor is firstly constructed and used as the judgment criterion to select the number of VMD decomposition layers adaptively;Secondly,VMD algorithm is used to decompose the echo signal into several modal components with a single modal component,and the useful echo components are extracted based on the features of the ultrasonic emission signal;Finally,the liquid density is calculated by extracting the amplitude and time of the echo from the modal components.The simulation results show that using the improved VMD to decompose the echo signal not only can improve the signal-to-noise ratio of the echo signal to 20.64 dB,but also can accurately obtain the echo information such as time and amplitude.Compared with the ensemble empirical mode decomposition(EEMD),this method effectively suppresses the modal aliasing,keeps the details of the signal to the maximum extent while suppressing noise,and improves the accuracy of the liquid density measurement.The density measurement accuracy can reach 0.21%of full scale.
