齿轮是最常用的传动部件,其常见的失效模式为齿面点蚀与齿根断裂。为了快速、精确地对其进行可靠性分析,提出了一种考虑失效相关的改进四阶矩法。首先,根据失效机制分别建立齿面接触疲劳与齿根弯曲断裂失效的可靠性模型。其次,为提高传...齿轮是最常用的传动部件,其常见的失效模式为齿面点蚀与齿根断裂。为了快速、精确地对其进行可靠性分析,提出了一种考虑失效相关的改进四阶矩法。首先,根据失效机制分别建立齿面接触疲劳与齿根弯曲断裂失效的可靠性模型。其次,为提高传统四阶矩求解精度,先使用改进一次二阶矩法求出的二阶可靠性指标,再求得新四阶可靠性指标,从而提高单一失效模式的可靠性求解精度;然后,基于条件概率降维法计算多失效模式间的相关度及考虑失效相关时的整体失效概率。最后,以某系统的传动齿轮为例,比较了所提方法、均值一次二阶矩(Mean Value First Order Second Moment,MVFOSM)法、改进一次二阶矩(Advanced First Order Second Moment,AFOSM)法和传统高阶矩标准化技术(High-Order Moment Standardization Technique,HOMST)法的计算效果。结果显示,所提方法在精度和效率上都得到了较大的提升,提供了一种分析多失效模式可靠性的理论方法。展开更多
This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sli...This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.展开更多
提出了机舱式激光雷达测风仪传动齿轮机械故障诊断方法。利用最小熵反褶积(MED)对齿轮的振动信号去噪处理,利用集成经验模态分解(EEMD)得到齿轮信号的内涵模态(IMF)分量,并根据IMF能量和齿轮峭度建立齿轮故障特征向量,将特征向量输入到...提出了机舱式激光雷达测风仪传动齿轮机械故障诊断方法。利用最小熵反褶积(MED)对齿轮的振动信号去噪处理,利用集成经验模态分解(EEMD)得到齿轮信号的内涵模态(IMF)分量,并根据IMF能量和齿轮峭度建立齿轮故障特征向量,将特征向量输入到最小二乘支持向量机(least squares support vector machine,LSSVM)中,完成传动齿轮机械故障的诊断。实验结果表明,该方法的齿轮故障诊断时间短,根据迭代次数的增加,误差率可控制在3%以下。展开更多
A new type of composite CVT(continuously variable transmission) systemsfeatured by power flow divergence and dual-mode convergence, capable of improving CVT's efficiencyand power capacity or making AMTs(automated ...A new type of composite CVT(continuously variable transmission) systemsfeatured by power flow divergence and dual-mode convergence, capable of improving CVT's efficiencyand power capacity or making AMTs(automated manual transmissions) become continuously variable, isstudied. With specific mechano-mechanical and electromechanical composite CVT systems as detailedexamples, its basic working principles are expatiated. General methods and key points in designingand realizing such systems are also analyzed and discussed.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
文摘齿轮是最常用的传动部件,其常见的失效模式为齿面点蚀与齿根断裂。为了快速、精确地对其进行可靠性分析,提出了一种考虑失效相关的改进四阶矩法。首先,根据失效机制分别建立齿面接触疲劳与齿根弯曲断裂失效的可靠性模型。其次,为提高传统四阶矩求解精度,先使用改进一次二阶矩法求出的二阶可靠性指标,再求得新四阶可靠性指标,从而提高单一失效模式的可靠性求解精度;然后,基于条件概率降维法计算多失效模式间的相关度及考虑失效相关时的整体失效概率。最后,以某系统的传动齿轮为例,比较了所提方法、均值一次二阶矩(Mean Value First Order Second Moment,MVFOSM)法、改进一次二阶矩(Advanced First Order Second Moment,AFOSM)法和传统高阶矩标准化技术(High-Order Moment Standardization Technique,HOMST)法的计算效果。结果显示,所提方法在精度和效率上都得到了较大的提升,提供了一种分析多失效模式可靠性的理论方法。
基金Supported by the National Natural Science Foundation of China(No.11172197 and No.11332008)a key-project grant from the Natural Science Foundation of Tianjin(No.010413595)
文摘This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.
文摘提出了机舱式激光雷达测风仪传动齿轮机械故障诊断方法。利用最小熵反褶积(MED)对齿轮的振动信号去噪处理,利用集成经验模态分解(EEMD)得到齿轮信号的内涵模态(IMF)分量,并根据IMF能量和齿轮峭度建立齿轮故障特征向量,将特征向量输入到最小二乘支持向量机(least squares support vector machine,LSSVM)中,完成传动齿轮机械故障的诊断。实验结果表明,该方法的齿轮故障诊断时间短,根据迭代次数的增加,误差率可控制在3%以下。
基金This project is supported by National Natural Science Foundation of China (No.50275053) and Provincial Natural Science Fundation of Guangdong (No.020857).
文摘A new type of composite CVT(continuously variable transmission) systemsfeatured by power flow divergence and dual-mode convergence, capable of improving CVT's efficiencyand power capacity or making AMTs(automated manual transmissions) become continuously variable, isstudied. With specific mechano-mechanical and electromechanical composite CVT systems as detailedexamples, its basic working principles are expatiated. General methods and key points in designingand realizing such systems are also analyzed and discussed.
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.
