Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition...Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.展开更多
Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its ...Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis(ICA).However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition(EMD) and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.展开更多
One of the important issues in the system identification and the spectrum analysis is the frequency resolution, i.e., the capability of distinguishing between two or more closely spaced frequency components. In the mo...One of the important issues in the system identification and the spectrum analysis is the frequency resolution, i.e., the capability of distinguishing between two or more closely spaced frequency components. In the modal identification by the empirical mode decomposition (EMD) method, because of the separating capability of the method, it is still a challenge to consistently and reliably identify the parameters of structures of which modes are not well separated. A new method is introduced to generate the intrin- sic mode functions (IMFs) through the filtering algorithm based on the wavelet packet decomposition (GIFWPD). In this paper, it is demonstrated that the CIFWPD method alone has a good capability of separating close modes, even under the severe condition beyond the critical frequency ratio limit which makes it impossible to separate two closely spaced harmonics by the EMD method. However, the GIFWPD-only based method is impelled to use a very fine sampling frequency with consequent prohibitive computational costs. Therefore, in order to decrease the computational load by reducing the amount of samples and improve the effectiveness of separation by increasing the frequency ratio, the present paper uses a combination of the complex envelope displacement analysis (CEDA) and the GIFWPD method. For the validation, two examples from the previous works are taken to show the results obtained by the GIFWPD-only based method and by combining the CEDA with the GIFWPD method.展开更多
A combination of the lattice Boltzmann method and the most recently developed dynamic mode decomposition is proposed for stability analysis. The simulations are performed on a graphical processing unit. Stability of t...A combination of the lattice Boltzmann method and the most recently developed dynamic mode decomposition is proposed for stability analysis. The simulations are performed on a graphical processing unit. Stability of the flow past a cylinder at supercritical state, Re = 50, is studied by the combination for both the exponential growing and the limit cycle regimes. The Ritz values, energy spectrum, and modes for both regimes are presented and compared with the Koopman eigenvalues. For harmonic-like periodic flow in the limit cycle, global analysis from the combination gives the same results as those from the Koopman analysis. For transient flow as in the exponential growth regime, the combination can provide more reasonable results. It is demonstrated that the combination of the lattice Boltzmann method and the dynamic mode decomposition is powerful and can be used for stability analysis for more complex flows.展开更多
In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals a...In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.展开更多
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat...On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.展开更多
Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable beari...Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to propose an adaptive variational mode decomposition (AVMD) technique for non-stationary signal analysis and bearing fault detection. The AVMD includes several steps in processing: 1) Signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to decompose the target signal and select the most representative intrinsic mode functions (IMFs). 3) The envelope spectrum analysis is performed using the selected IMFs to identify the characteristic features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques.展开更多
Large atmospheric boundary layer fluctuations and smaller turbine-scale vorticity dynamics are separately hypothesized to initiate the wind turbine wake meandering phenomenon,a coherent,dynamic,turbine-scale oscillati...Large atmospheric boundary layer fluctuations and smaller turbine-scale vorticity dynamics are separately hypothesized to initiate the wind turbine wake meandering phenomenon,a coherent,dynamic,turbine-scale oscillation of the far wake.Triadic interactions,the mechanism of energy transfers between scales,manifest as triples of wavenumbers or frequencies and can be characterized through bispectral analyses.The bispectrum,which correlates the two frequencies to their sum,is calculated by two recently developed multi-dimensional modal decomposition methods:scale-specific energy transfer method and bispectral mode decomposition.Large-eddy simulation of a utility-scale wind turbine in an atmospheric boundary layer with a broad range of large length-scales is used to acquire instantaneous velocity snapshots.The bispectrum from both methods identifies prominent upwind and wake meandering interactions that create a broad range of energy scales including the wake meandering scale.The coherent kinetic energy associated with the interactions shows strong correlation between upwind scales and wake meandering.展开更多
Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller b...Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.展开更多
In order to eliminate noise interference of metal magnetic memory signal in early diagnosis of stress concentration zones and metal defects, the empirical mode decomposition method combined with the magnetic field gra...In order to eliminate noise interference of metal magnetic memory signal in early diagnosis of stress concentration zones and metal defects, the empirical mode decomposition method combined with the magnetic field gradient characteristic was proposed. A compressive force periodically acting upon a casing pipe led to appreciable deformation, and magnetic signals were measured by a magnetic indicator TSC-1M-4. The raw magnetic memory signal was first decomposed into different intrinsic mode functions and a residue, and the magnetic field gradient distribution of the subsequent reconstructed signal was obtained. The experimental results show that the gradient around 350 mm represents the maximum value ignoring the marginal effect, and there is a good correlation between the real maximum field gradient and the stress concentration zone. The wavelet transform associated with envelop analysis also exhibits this gradient characteristic, indicating that the proposed method is effective for early identifying critical zones.展开更多
Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar(SAR) sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark...Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar(SAR) sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.展开更多
Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and b...Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and braking)of the locomotive,the passing frequencies of a polygonal wheel will exhibit time-varying behaviors,which makes it too difficult to effectively detect the wheel defect.Moreover,most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels.To address these issues,this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition(ACMD)approach.Firstly,a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor.After the rotating frequency is obtained,signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear.Finally,the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes.Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.展开更多
A new algorithm, named segmented second empirical mode decomposition (EMD) algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency ...A new algorithm, named segmented second empirical mode decomposition (EMD) algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.展开更多
After an aerial object enters the water, physical changes to sounds in the water caused by the accompanying bubbles are quite complex. As a result, traditional signal analyzing methods cannot identify the real physica...After an aerial object enters the water, physical changes to sounds in the water caused by the accompanying bubbles are quite complex. As a result, traditional signal analyzing methods cannot identify the real physical object. In view of this situation, a novel method for analyzing the sounds caused by an aerial object’s entry into water was proposed. This method analyzes the vibrational mode of the bubbles by using empitical mode decomposition. Experimental results showed that this method can efficiently remove noise and extract the broadband pulse signal and low-frequency fluctuating signal, producing an accurate resolution of entry time and frequency. This shows the improved performance of the proposed method.展开更多
由于用户级综合能源系统(integrated energy system,IES)的多元负荷序列之间复杂的耦合关系及易受外部因素影响等原因,综合能源系统多元负荷的精准预测面临很大困难。为此,提出一种基于Spearman相关性分析阈值寻优(threshold optimizati...由于用户级综合能源系统(integrated energy system,IES)的多元负荷序列之间复杂的耦合关系及易受外部因素影响等原因,综合能源系统多元负荷的精准预测面临很大困难。为此,提出一种基于Spearman相关性分析阈值寻优(threshold optimization,TO)和变分模态分解结合长短期记忆网络(variational mode decomposition based long short-term memory network,VMD-LSTM)的多元负荷预测方法。首先,使用斯皮尔曼等级(Spearman rank,SR)相关系数定量计算多元负荷间以及负荷与其他气候因素间的相关关系并通过循环寻优确定最优相关阈值,然后采用VMD算法将以最优阈值筛选出的负荷特征序列分解成更简单、平稳、有规律性的本征模态函数(intrinsic mode function,IMF)后与最优气象特征一起输入LSTM模型进行负荷预测。通过某用户级IES的实际数据对所提方法的有效性进行了验证,结果表明,所提方法能有效提高IES的多元负荷预测精度。展开更多
文摘Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.
基金supported by National Natural Science Foundation of China (Grant No. 50975192)Tianjin Municipal Natural Science Foundation of China (Grant No. 10YFJZJC14100)
文摘Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis(ICA).However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition(EMD) and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.
基金supported by the State Key Program of National Natural Science of China (No. 11232009)the Shanghai Leading Academic Discipline Project (No. S30106)
文摘One of the important issues in the system identification and the spectrum analysis is the frequency resolution, i.e., the capability of distinguishing between two or more closely spaced frequency components. In the modal identification by the empirical mode decomposition (EMD) method, because of the separating capability of the method, it is still a challenge to consistently and reliably identify the parameters of structures of which modes are not well separated. A new method is introduced to generate the intrin- sic mode functions (IMFs) through the filtering algorithm based on the wavelet packet decomposition (GIFWPD). In this paper, it is demonstrated that the CIFWPD method alone has a good capability of separating close modes, even under the severe condition beyond the critical frequency ratio limit which makes it impossible to separate two closely spaced harmonics by the EMD method. However, the GIFWPD-only based method is impelled to use a very fine sampling frequency with consequent prohibitive computational costs. Therefore, in order to decrease the computational load by reducing the amount of samples and improve the effectiveness of separation by increasing the frequency ratio, the present paper uses a combination of the complex envelope displacement analysis (CEDA) and the GIFWPD method. For the validation, two examples from the previous works are taken to show the results obtained by the GIFWPD-only based method and by combining the CEDA with the GIFWPD method.
文摘A combination of the lattice Boltzmann method and the most recently developed dynamic mode decomposition is proposed for stability analysis. The simulations are performed on a graphical processing unit. Stability of the flow past a cylinder at supercritical state, Re = 50, is studied by the combination for both the exponential growing and the limit cycle regimes. The Ritz values, energy spectrum, and modes for both regimes are presented and compared with the Koopman eigenvalues. For harmonic-like periodic flow in the limit cycle, global analysis from the combination gives the same results as those from the Koopman analysis. For transient flow as in the exponential growth regime, the combination can provide more reasonable results. It is demonstrated that the combination of the lattice Boltzmann method and the dynamic mode decomposition is powerful and can be used for stability analysis for more complex flows.
基金supported by the National Science and Technology,China(Grant No.2012BAJ15B04)the National Natural Science Foundation of China(Grant Nos.41071270 and 61473213)+3 种基金the Natural Science Foundation of Hubei Province,China(Grant No.2015CFB424)the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics,China(Grant No.SOED1405)the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science,China(Grant No.Z201303)the Hubei Key Laboratory Foundation of Transportation Internet of Things,Wuhan University of Technology,China(Grant No.2015III015-B02)
文摘In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.
基金supported by the Social Science Foundation of China under Grant No.17BGL231。
文摘On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.
文摘Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to propose an adaptive variational mode decomposition (AVMD) technique for non-stationary signal analysis and bearing fault detection. The AVMD includes several steps in processing: 1) Signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to decompose the target signal and select the most representative intrinsic mode functions (IMFs). 3) The envelope spectrum analysis is performed using the selected IMFs to identify the characteristic features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques.
基金supported by the National Science Foundation(Grant No.21-36371)supported by the National Science Foundation(Grant Nos.21-38259,21-38286,21-38307,21-37603,and 21-38296)。
文摘Large atmospheric boundary layer fluctuations and smaller turbine-scale vorticity dynamics are separately hypothesized to initiate the wind turbine wake meandering phenomenon,a coherent,dynamic,turbine-scale oscillation of the far wake.Triadic interactions,the mechanism of energy transfers between scales,manifest as triples of wavenumbers or frequencies and can be characterized through bispectral analyses.The bispectrum,which correlates the two frequencies to their sum,is calculated by two recently developed multi-dimensional modal decomposition methods:scale-specific energy transfer method and bispectral mode decomposition.Large-eddy simulation of a utility-scale wind turbine in an atmospheric boundary layer with a broad range of large length-scales is used to acquire instantaneous velocity snapshots.The bispectrum from both methods identifies prominent upwind and wake meandering interactions that create a broad range of energy scales including the wake meandering scale.The coherent kinetic energy associated with the interactions shows strong correlation between upwind scales and wake meandering.
基金This project is supported by National Natural Science Foundation of China (No.50205050).
文摘Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.
基金Project(10772061) supported by the National Natural Science Foundation of ChinaProject(A200907) supported by the Natural Science Foundation of Heilongjiang Province, China Project(20092322120001) supported by the PhD Programs Foundations of Ministry of Education of China
文摘In order to eliminate noise interference of metal magnetic memory signal in early diagnosis of stress concentration zones and metal defects, the empirical mode decomposition method combined with the magnetic field gradient characteristic was proposed. A compressive force periodically acting upon a casing pipe led to appreciable deformation, and magnetic signals were measured by a magnetic indicator TSC-1M-4. The raw magnetic memory signal was first decomposed into different intrinsic mode functions and a residue, and the magnetic field gradient distribution of the subsequent reconstructed signal was obtained. The experimental results show that the gradient around 350 mm represents the maximum value ignoring the marginal effect, and there is a good correlation between the real maximum field gradient and the stress concentration zone. The wavelet transform associated with envelop analysis also exhibits this gradient characteristic, indicating that the proposed method is effective for early identifying critical zones.
基金The National Science and Technology Support Project under contract No.2014BAB12B02the Natural Science Foundation of Liaoning Province under contract No.201602042
文摘Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar(SAR) sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.52005416,51735012,and 51825504)the Sichuan Science and Technology Program(Grant No.2020YJ0213)+1 种基金the Fundamental Research Funds for the Central Universities,SWJTU(Grant No.2682021CX091)the State Key Laboratory of Traction Power(Grant No.2020TPL-T 11).
文摘Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and braking)of the locomotive,the passing frequencies of a polygonal wheel will exhibit time-varying behaviors,which makes it too difficult to effectively detect the wheel defect.Moreover,most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels.To address these issues,this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition(ACMD)approach.Firstly,a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor.After the rotating frequency is obtained,signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear.Finally,the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes.Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.
文摘A new algorithm, named segmented second empirical mode decomposition (EMD) algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.
文摘After an aerial object enters the water, physical changes to sounds in the water caused by the accompanying bubbles are quite complex. As a result, traditional signal analyzing methods cannot identify the real physical object. In view of this situation, a novel method for analyzing the sounds caused by an aerial object’s entry into water was proposed. This method analyzes the vibrational mode of the bubbles by using empitical mode decomposition. Experimental results showed that this method can efficiently remove noise and extract the broadband pulse signal and low-frequency fluctuating signal, producing an accurate resolution of entry time and frequency. This shows the improved performance of the proposed method.
文摘由于用户级综合能源系统(integrated energy system,IES)的多元负荷序列之间复杂的耦合关系及易受外部因素影响等原因,综合能源系统多元负荷的精准预测面临很大困难。为此,提出一种基于Spearman相关性分析阈值寻优(threshold optimization,TO)和变分模态分解结合长短期记忆网络(variational mode decomposition based long short-term memory network,VMD-LSTM)的多元负荷预测方法。首先,使用斯皮尔曼等级(Spearman rank,SR)相关系数定量计算多元负荷间以及负荷与其他气候因素间的相关关系并通过循环寻优确定最优相关阈值,然后采用VMD算法将以最优阈值筛选出的负荷特征序列分解成更简单、平稳、有规律性的本征模态函数(intrinsic mode function,IMF)后与最优气象特征一起输入LSTM模型进行负荷预测。通过某用户级IES的实际数据对所提方法的有效性进行了验证,结果表明,所提方法能有效提高IES的多元负荷预测精度。