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 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
随着西藏地区旅游业的蓬勃发展,人群荷载对藏式古建筑结构安全的影响日益显著。为了量化人群荷载对结构的影响,需要从结构健康监测数据中分离出人群荷载引起的应变响应,提出了一种基于灰狼优化(grey wolf optimizer,GWO)算法优化变分模...随着西藏地区旅游业的蓬勃发展,人群荷载对藏式古建筑结构安全的影响日益显著。为了量化人群荷载对结构的影响,需要从结构健康监测数据中分离出人群荷载引起的应变响应,提出了一种基于灰狼优化(grey wolf optimizer,GWO)算法优化变分模态分解(variational mode decomposition,VMD)算法并结合GG(Gath-Geva,GG)聚类算法的人群荷载效应分离方法,简称GWO-VMD-GG。首先,利用GWO算法以最小包络熵为适应度函数来确定VMD参数模态分解层数K和二次惩罚因子α;其次,采用优化后的VMD算法对实测应变信号进行分解;最后,以相关系数为特征参数,采用GG聚类算法对分解得到的本征模态函数(intrinsic mode function,IMF)分量进行聚类,将快变应变分量重构,得到人群荷载引起的应变响应。简述了某藏式古建筑游客分布特征,并通过对藏式古建筑木结构应变监测数据的分析,成功分离出游客日、周、年分布特征人群荷载效应,验证了所提方法在工程实践中的有效性。结果表明,该方法能够有效避免VMD参数选择和IMF分量划分过程中的人为干预,实现从大规模监测数据中自动分离人群荷载效应,为藏式古建筑的结构安全评估提供了一种有效手段。展开更多
Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing metho...Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.展开更多
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 in part by the Science and Technology Major Project of Anhui Province(Grant No.17030901037)in part by the Humanities and Social Science Fund of Ministry of Education of China(Grant No.19YJAZH098)+2 种基金in part by the Program for Synergy Innovation in the Anhui Higher Education Institutions of China(Grant Nos.GXXT-2020-012,GXXT-2021-044)in part by Science and Technology Planning Project of Wuhu City,Anhui Province,China(Grant No.2021jc1-2)part by Research Start-Up Fund for Introducing Talents from Anhui Polytechnic University(Grant No.2021YQQ066).
文摘Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.
