变压器等电气设备的吊装、转运环节是疏于监控的薄弱环节,极易发生由机械冲击引起的二次损伤。对变压器轨道运输车行进过程中受路基振动引起的冲击响应开展研究。首先,建立了轨道运输车⁃变压器耦合分析模型,利用有限元分析得出轨道运输...变压器等电气设备的吊装、转运环节是疏于监控的薄弱环节,极易发生由机械冲击引起的二次损伤。对变压器轨道运输车行进过程中受路基振动引起的冲击响应开展研究。首先,建立了轨道运输车⁃变压器耦合分析模型,利用有限元分析得出轨道运输车⁃变压器耦合分析模型在路基振动作用下的核心响应区域。然后,提出了一种基于小波包散布熵的非周期瞬态响应特征提取方法。该方法通过小波包最优子带树结构对整个频带进行良好的稀疏性分割,将包含多种信息的一维数据分解到不同维度,实现信号的有效分解,通过Teager能量算子(Teager Energy Operator,TEO)增强子带信号的冲击特性,利用散布熵选取包含冲击响应特征的子带信号。最后,通过路基振动仿真信号验证了所提方法能够准确从耦合路径干扰中提取出非周期性瞬态冲击响应成分。展开更多
Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent ...Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent secondary damage,thereby avoiding heavy conse-quences.In this study,fault diagnosis of tractor auxiliary gearbox is presented.Vibration signals of healthy and faulty pinions gear under three different operational conditions(Rotational speeds of 600 RPM,1350 RPM and 2000 RPM)were collected,and discrete wave-let transform(DWT)was used as signal processing.Useful statistical features were calcu-lated from collected signals.Correlation-based feature selection(CFS)method was used to find the best features.Random forest(RF)and multilayer perceptron(MLP)neural net-works were employed to classify the data.The overall accuracy of RF classifier without using feature selection were 86.25%,at 600 RPM.The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.The best results obtained at 1350 RPM,since the detection accuracy was 95%.The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.展开更多
文摘变压器等电气设备的吊装、转运环节是疏于监控的薄弱环节,极易发生由机械冲击引起的二次损伤。对变压器轨道运输车行进过程中受路基振动引起的冲击响应开展研究。首先,建立了轨道运输车⁃变压器耦合分析模型,利用有限元分析得出轨道运输车⁃变压器耦合分析模型在路基振动作用下的核心响应区域。然后,提出了一种基于小波包散布熵的非周期瞬态响应特征提取方法。该方法通过小波包最优子带树结构对整个频带进行良好的稀疏性分割,将包含多种信息的一维数据分解到不同维度,实现信号的有效分解,通过Teager能量算子(Teager Energy Operator,TEO)增强子带信号的冲击特性,利用散布熵选取包含冲击响应特征的子带信号。最后,通过路基振动仿真信号验证了所提方法能够准确从耦合路径干扰中提取出非周期性瞬态冲击响应成分。
文摘Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent secondary damage,thereby avoiding heavy conse-quences.In this study,fault diagnosis of tractor auxiliary gearbox is presented.Vibration signals of healthy and faulty pinions gear under three different operational conditions(Rotational speeds of 600 RPM,1350 RPM and 2000 RPM)were collected,and discrete wave-let transform(DWT)was used as signal processing.Useful statistical features were calcu-lated from collected signals.Correlation-based feature selection(CFS)method was used to find the best features.Random forest(RF)and multilayer perceptron(MLP)neural net-works were employed to classify the data.The overall accuracy of RF classifier without using feature selection were 86.25%,at 600 RPM.The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.The best results obtained at 1350 RPM,since the detection accuracy was 95%.The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.
