In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD ...In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD is applied to the feature extraction of vehicle vibration signals. First, the multi-autocorrelation method is adopted in each input signal,so the noise is reduced effectively. Then, EMD is used to deal with these signals,and the intrinsic mode functions （IMFs） are obtained. Finally, for obtaining the feature information of these signals, the Hilbert transformation and the spectrum analysis are performed in some IMFs. Theoretical analysis and ex- periment verify the effectiveness of the method, which are valuable reference for the same engineering problems.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
In order to deal with the non-stationary characteristics of blasting vibration signals and the end issue in the empirical mode decomposition(EMD), an improved endpoint continuation method is proposed. First, the linea...In order to deal with the non-stationary characteristics of blasting vibration signals and the end issue in the empirical mode decomposition(EMD), an improved endpoint continuation method is proposed. First, the linear continuation method of extreme points is used to determine the extremum of the signal endpoint fast. Secondly, the extreme points of transition section outside the signal ends are obtained by a mirror continuation method of extreme points, and then the envelope and continuation curve of the transition section of the signal are constructed. Lastly, the sinusoid of the stationary section outside the signal is constructed to achieve the continuation curve from the transition section to the stationary section. Based on the "singular extreme points" phenomenon of blasting vibration signal, the negative maxima and positive minimum are eliminated, then the maximum and minimum are guaranteed to appear at intervals. Thus,the number of iterations is reduced and the instability of EMD decomposition is improved. The calculation formula of amplitude, cycle and initial phase are given for the transition section and stationary section outside the signal. The endpoint processing effect of the simulated signal and the measured blasting vibration signal show that the improved endpoint continuation method can suppress the signal endpoint effect well.展开更多
在公路隧道爆破中,为了获得准确、真实的振动特征,基于鲁棒性局部均值分解(robust local mean decomposition,RLMD)和经验小波变换(empirical wavelet transform,EWT),建立了一种RLMD-EWT联合降噪方法。首先,将实测信号进行RLMD分解,得...在公路隧道爆破中,为了获得准确、真实的振动特征,基于鲁棒性局部均值分解(robust local mean decomposition,RLMD)和经验小波变换(empirical wavelet transform,EWT),建立了一种RLMD-EWT联合降噪方法。首先,将实测信号进行RLMD分解,得到若干乘积函数(product functions,PF)分量,结合相关系数和样本熵(sample entropy,SE)对PF分量进行分类,对含噪分量进行EWT分解,进而实现降噪目标。通过降噪效果对比,RLMD-EWT联合降噪方法具备可行性,相较LMD、EWT、RLMD和LMD-WT方法,表现出更优的降噪性能、更高的降噪效率和准确度。结合HHT频谱图,RLMD-EWT方法对于30~50 Hz、250 Hz以上2个频段的噪声可实现有效滤除,具备良好的信号适用度。展开更多
基金Supported by the Scientific Research Foundation for the Imported Talents(YKJ201014)~~
文摘In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD is applied to the feature extraction of vehicle vibration signals. First, the multi-autocorrelation method is adopted in each input signal,so the noise is reduced effectively. Then, EMD is used to deal with these signals,and the intrinsic mode functions （IMFs） are obtained. Finally, for obtaining the feature information of these signals, the Hilbert transformation and the spectrum analysis are performed in some IMFs. Theoretical analysis and ex- periment verify the effectiveness of the method, which are valuable reference for the same engineering problems.
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.
基金Supported by the National Natural Science Foundation of China(51374212)
文摘In order to deal with the non-stationary characteristics of blasting vibration signals and the end issue in the empirical mode decomposition(EMD), an improved endpoint continuation method is proposed. First, the linear continuation method of extreme points is used to determine the extremum of the signal endpoint fast. Secondly, the extreme points of transition section outside the signal ends are obtained by a mirror continuation method of extreme points, and then the envelope and continuation curve of the transition section of the signal are constructed. Lastly, the sinusoid of the stationary section outside the signal is constructed to achieve the continuation curve from the transition section to the stationary section. Based on the "singular extreme points" phenomenon of blasting vibration signal, the negative maxima and positive minimum are eliminated, then the maximum and minimum are guaranteed to appear at intervals. Thus,the number of iterations is reduced and the instability of EMD decomposition is improved. The calculation formula of amplitude, cycle and initial phase are given for the transition section and stationary section outside the signal. The endpoint processing effect of the simulated signal and the measured blasting vibration signal show that the improved endpoint continuation method can suppress the signal endpoint effect well.
文摘在公路隧道爆破中,为了获得准确、真实的振动特征,基于鲁棒性局部均值分解(robust local mean decomposition,RLMD)和经验小波变换(empirical wavelet transform,EWT),建立了一种RLMD-EWT联合降噪方法。首先,将实测信号进行RLMD分解,得到若干乘积函数(product functions,PF)分量,结合相关系数和样本熵(sample entropy,SE)对PF分量进行分类,对含噪分量进行EWT分解,进而实现降噪目标。通过降噪效果对比,RLMD-EWT联合降噪方法具备可行性,相较LMD、EWT、RLMD和LMD-WT方法,表现出更优的降噪性能、更高的降噪效率和准确度。结合HHT频谱图,RLMD-EWT方法对于30~50 Hz、250 Hz以上2个频段的噪声可实现有效滤除,具备良好的信号适用度。
