共查询到19条相似文献,搜索用时 171 毫秒
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为了准确地进行齿轮故障诊断,结合变分模态分解和最小熵解卷积,给出了一种新的故障诊断方法。首先,以包含啮合频率的分量的包络峭度最大作为原则,确定变分模态分解的分量个数;然后,将齿轮振动信号运用变分模态分解,得到多个分量;选取包含啮合频率的分量作为敏感分量;接着,应用最小熵解卷积,将敏感分量降噪;最后,应用包络分析技术进行故障诊断。通过齿轮断齿故障振动数据的分析,验证了方法的有效性。 相似文献
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对于受迫振动系统,当激励频率增加并且通过共振(临界)频率时,系统的振幅将达到其峰值.通过相位调制可以有效地减小共振振幅,其机理是振幅的变化取决于相应和激励之间的相位差,而此相位差可以通过控制激励频率的变化规律进行调制.该方法由受变频率脉冲激励的悬臂梁进行了实验验证.实验结果表明,对于给定的最大频率增加速率,通过相位调制可以将共振振幅减小18%左右. 相似文献
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提出了一种全新的基于多小波包的齿轮箱故障诊断方法.在机械故障诊断领域,小波分析已经被应用在利用齿轮振动信号进行的故障诊断中.由于小波函数空间在高尺度上频带较宽,因此隐藏在信号中的在较高频段发生的窄带故障信号的频率成分不能被精确地诊断.提出的多小波变换,通过划分小波变换不同层的频带克服了单小波(标量小波)系统的这种缺陷.通过多小波包变换,可以自动并精确地划分不同小波包结点的频率段,从而对频率段较窄的瞬变故障信号进行精确的诊断.对齿轮箱的仿真实验结果表明,应用多小波包系统,不但可以对齿轮系统中包含瞬变现象的故障信号进行诊断,而且可以精确确定齿轮中坏齿的位置. 相似文献
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研究一种用于涡街流量计涡街频率测量的随机共振信号处理方法.探讨了信号与双稳系统的阈值和克莱默斯逃逸率之间的关系,通过外加幅值和频率可调的信号进行双重调制,实现信号与双稳系统的匹配,数值仿真表明该匹配方式是可行和有效的.针对涡街信号,提出基于标准差的调制参数选取方法,该方法利用涡街信号特征,通过信号标准差估计管道内流速,缩小了调制参数的选取范围,提高了信号与双稳系统匹配的效率.实验结果表明,利用标准差选取调制参数对涡街信号进行调制,可以有效地使涡街信号,特别是弱涡街信号产生随机共振,进而检测到涡街频率,实现流量测量. 相似文献
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作为直升机上重要的关键部件,直升机齿轮箱能够将动力转换为动力输出形式,从而满足不同形式下动力的需要;针对直升机齿轮箱状态无法准确预测的技术难题,将灰色系统理论中的灰色预测方法运用到直升机齿轮箱中,有效解决了齿轮箱使用状态难以准确预测的技术难题;首先对采集到的直升机齿轮箱的不同的振动信号进行特征提取,然后采用信息融合技术,将不同振动信号的特征值进行融合,最后运用灰色预测方法对直升机齿轮箱的使用状态进行预测;文中对所提出的方法进行了试验验证,结果表明,所提出的基于灰色预测的直升机齿轮箱状态预测方法能够实现对直升机齿轮箱的状态准确预测的效能,并对其他航空设备以及机械设备的状态预测具有一定的借鉴意义。 相似文献
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针对齿轮故障特征信息往往被信号中的噪声淹没的问题,提出了一种基于谐波小波包、样本熵和灰色关联度的齿轮故障识别方法。首先,采用顺序形态滤波器,并结合实际选用最简单的直线结构元素,对实测齿轮振动信号进行顺序形态滤波降噪预处理。然后,采用谐波小波包将不同故障的齿轮振动信号分解到3层共8个频带上,并计算各频带的样本熵。最后,以样本熵为元素构造特征向量,通过计算标准故障模式特征向量与待识别样本的灰色关联度来判断齿轮的工作状态和故障类型。试验结果表明,该方法能够有效地应用于齿轮系统的故障诊断。 相似文献
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李亚利 《计算机测量与控制》2020,28(6):7-11
针对短丝纤维卷绕牵伸齿轮箱故障信号不易提取的问题,提出了基于图像纹理信息的特征提取方法。通过对齿轮箱振动信号进行小波包双谱分析,获得具有稳定纹理信息的振动信号双谱图,采用基于小波变换对双谱图进行图像融合,提高图像的综合纹理特征。采用灰度共生矩阵的四个特征参数对振动信号的双谱图进行加权融合特征提取。在短丝生产线上对齿轮箱常见的齿轮破损和裂纹进行了实验分析,结果表明本文方法的故障识别率达到85%以上。 相似文献
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提出了一种基于扩展广义多重分形维数算法的汽车变速箱故障诊断方法。该算法是基于传统的G-P关联维数算法扩展而形成的,通过该算法对变速箱上采集的不同工作状态下的振动信号进行处理,提取变速箱齿轮的振动信号的分数维,观察及分析分形维数与变速箱齿轮的磨损规律的关系,发现其反映变速箱齿轮的真实运行状态,故可以此作为齿轮磨损预测和诊断的有效依据。 相似文献
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Hilbert-小波变换的齿轮箱故障诊断* 总被引:1,自引:0,他引:1
采用希尔伯特—小波变换对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用小波变换分解获得振动响应信号的各层高频信号小波系数和低频信号小波系数,对小波系数进行重构获得具有不同特征时间尺度的各高频信号和低频信号;再对分解的信号进行希尔伯特变换获得时频信息谱以提取系统的统计特征信息,实现监测齿轮运转工作状态,及时发现齿轮的早期故障,提高机械运行的安全性。仿真研究结果表明,小波变换分解和希尔伯特边际谱方法在故障信息诊断方面是可行和有效的,提高了故障检测的可靠性。 相似文献
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Local mean decomposition (LMD) is a novel self-adaptive time–frequency analysis method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals. By using LMD, any complicated signal can be decomposed into a number of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated signal from which physically meaningful instantaneous frequencies can be obtained. In fact, each PF is just a mono-component AM–FM signal. Therefore, the procedure of LMD may be regarded as the process of demodulation. While fault occurs in gear or roller bearing, the vibration signals picked up would exactly display AM–FM characteristics. So it is possible to diagnose gear and roller bearing fault by LMD. Targeting the modulation features of the gear or roller bearing fault vibration signal, a rotating machinery fault diagnosis method based on LMD is proposed. In this paper, firstly the LMD method is introduced; secondly, the LMD method is compared with another competing time–frequency analysis approach, namely, empirical mode decomposition (EMD) method and the results show the superiority of the LMD method; finally, the LMD method is applied to the gear and roller bearing fault diagnosis. The analysis results from the practical gearbox vibration signal demonstrate that the diagnosis approach based on LMD could identify gear and roller bearing work condition accurately and effectively. 相似文献
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The gearbox is an important component in industrial drives, providing safe and reliable operation for industrial production. Wavelet packet transform (WPT) analysis was used to extract fault features in the vibration signals generated by a gearbox. The extracted features from the WPT were used as input in a rough set (RS) for attribute reduction and then combined with a genetic algorithm to obtain global optimal attribute reduction results. The fault features gained after the attribute reductions were used to generate decision rules. The unknown gear status signal attributes were used as input to match the generated decision rules for fault diagnosis purposes. Gearbox vibration signals contain a significant amount of gear status information; a WPT has an acute portion-locked ability to extract attribute information from the vibration signals. However, WPT frequency aliasing would lead to the generation of spurious frequency components, affecting gear fault diagnosis. In this paper, we introduce an improved WPT to eliminate frequency aliasing, thus improving the accuracy of fault diagnosis. This paper studies the use of wavelet packet for feature extraction and the RS for classification; the results demonstrate that this method can accurately and reliably detect failure modes in a gearbox. 相似文献
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The requirement for higher energy density transmissions (lower weight) in helicopters has led to the development of the split
torque gearbox (STG) to replace the traditional planetary gearbox by the drive train designer. This may pose a challenge for
the current gear analysis methods used in health and usage monitoring systems (HUMS). Gear analysis uses time synchronous
averages to separates in frequency gears that are physically close to a sensor. The effect of a large number of synchronous
components (gears or bearing) in close proximity may significantly reduce the fault signal (reduce signal to noise ratio)
and therefore reduce the effectiveness of current gear analysis algorithms. In this paper, quantification of condition indicator
performance on a split torque gearbox is reported. The vibration signatures are processed through a number of gear analysis
algorithms to quantify the gear fault performance. The performance metric is separability. 相似文献
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为分析多级平行轴齿轮传动系统内部难以直接测量的振动信号,建立了4级齿轮传动系统非线性动力学46自由度仿真模型,并基于势能法对时变啮合刚度进行精确求解,采用4阶Runge-Kutta法对集中参数模型进行Matlab编程,进而分析时变啮合刚度对模型响应结果的影响.基于分析结果,探究了引起多级平行轴齿轮传动系统振动的重要原因.同时,通过模型振动时域响应及频谱分析,发现各级传动齿轮之间存在耦合振动现象,能够为实际多级平行轴齿轮传动系统健康监测与故障诊断提供理论支撑与科学指导. 相似文献