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《内燃机学报》2016,(3)
气门间隙异常是柴油机常见机械故障之一,对其进行准确的诊断对提高柴油机的使用寿命具有积极的作用.针对柴油机气门间隙异常的问题,在某直列6缸柴油机上模拟了不同气门故障,提出了基于双谱估计、图像处理以及分形理论相结合的故障诊断方法.该方法首先利用双谱估计对非线性、非高斯信号的敏感性质,分析了不同故障状态下振动信号中非高斯成分及二次相位耦合特性,然后通过图像处理技术将双谱图表示为以像素位置及对应颜色强度构成的三维空间曲面,最后利用分形理论提取该曲面的分形盒维数作为故障特征.结果表明:不同状态下柴油机振动信号的双谱及其图像分形维数明显可分,正常状态下的双谱峰值分布最为复杂、分形维数最大,故障状态下的分形维数分别处在不同的范围.因此,以振动信号的分形维数作为特征值可实现柴油机气门故障诊断. 相似文献
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风力机齿轮箱振动信号是一种时频特性复杂的非平稳信号,常规的时域和频域分析方法难以有效的分析齿轮箱故障及提取故障特征。提出一种基于小波分析和神经网络的风力机齿轮箱故障诊断方法,该方法采用小波时频分析技术对风力发电机故障振动信号进行消噪滤波,通过小波包分解系数求取频带能量,根据各个频带能量的变化提取故障特征,为实现智能诊断提供故障特征值。应用BP神经网络进行故障识别,并采用LabVIEW和matlab软件予以实现。结果表明,该方法能有效提高风力发电机组齿轮箱故障诊断的准确性。 相似文献
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针对小波包分解振动信号时会产生频谱混叠从而导致齿轮箱复合故障特征能量谱提取困难的问题,提出基于旁路滤波改进小波包的方法对双馈风电机组齿轮箱复合故障振动信号进行研究,并以风电场的大量齿轮箱振动信号为基础,运用传统小波包及旁路滤波改进小波包分别对齿轮箱振动信号提取特征能量谱。实验结果表明:运用旁路滤波改进小波包对双馈风电机组齿轮箱复合故障振动信号进行分析,可有效避免传统小波包分析振动信号的频谱混叠现象,准确提取每种故障状态的特征能量谱。 相似文献
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基于振动信号的柴油机供油提前角检测研究 总被引:1,自引:0,他引:1
供油提前角是监测柴油机技术状况变化或检测其故障的有效参数之一,因此研究快速、准确地检测供油提前角的方法是有重要意义的。阐述了一种基于高压油管振动信号的间接检测方法。讨论了实车上测量高压油管振动信号的方法;对实车检测到的油管振动信号进行了自适应滤波处理;对处理后的信号进行了小波变换,保留了一定的低频成分,从而提取出供油始点,并结合上止点信号计算出了供油提前角。试验证明,利用振动信号可以准确计算出柴油机的供油提前角,实现供油提前角的不解体诊断。 相似文献
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导致柴油机同一故障的各相关检测信号使其融合,应用在神经网络故障识别系统中,可以快速、准确地诊断柴油机故障.本文主要融合气缸振动信号、喷油信号及水温信号,设计了通过提取信号特征值,应用神经网络识别系统诊断柴油机功率下降故障的方法.信号特征值分别按各自信号的特点提取,其中气缸振动信号以振动信号的各子带功率谱为特征,即在频域中选取特征;喷油信号以信号波形形状识别为特征,即在时域中选取特征;水温信号因其为慢速变化信号,取不同阶段的水温信号为特征. 相似文献
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Planetary gearboxes play an important role in wind turbine (WT) drivetrains. WTs usually work under time-varying running conditions due to the volatile wind conditions. The planetary gearbox vibration signals in such an environment are hence highly nonstationary. Conventional spectral analysis and demodulation analysis methods are unable to identify the characteristic frequency of gear fault from such nonstationary signals. As such, this paper presents a time–frequency analysis methods to reveal the constituent frequency components of nonstationary signals and their time-varying features for WT planetary gearbox monitoring. More specifically, we exploit the adaptive optimal kernel (AOK) method for this challenging application because of its fine time–frequency resolution and cross-term free nature, as demonstrated by our simulation analysis. In this study, the AOK method has been applied to identify the time-varying characteristic frequencies of gear fault or to extract different levels of impulses induced by gear faults from lab WT experimental signals and in-situ WT signals under time-varying running conditions. We have demonstrated that the AOK is effective diagnosis of: (a) both the local damage (a single chipped tooth) and distributed faults (wear of all teeth), (b) both sun gear and planet gear faults, and (c) faults with very weak signature (e.g., the sun gear fault at the low speed stage of a WT planetary gearbox). 相似文献
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为解决风电机组传动链易发生故障的问题,文章阐述了风电机组齿轮箱特征频率的计算方法和基于振动信号分析的故障特征提取方法。结合实际情况,以行星级齿轮磨损、中间轴小齿轮崩齿、高速轴齿轮崩齿和发电机轴承电腐蚀等典型故障为例,通过齿轮箱特征频率和传动链典型故障振动信号基本特征分析,可较好地完成故障识别。结果表明,采用经典信号处理方法能对上述典型故障进行特征提取,验证了经典方法对单一、明显故障特征提取的有效性,为深入开展传动链故障特征提取方法研究奠定了基础,为风电机组故障检修维护提供了技术支撑。 相似文献
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针对柴油机振动信号的瞬时非线性特点,提出采用柴油机振动信号的本征模函数(IMF)分量进行特征频带识别的新方法。将柴油机振动信号经经验模态分解,并去掉主要干扰因素所对应的IMF分量,再将剩余IMF分量进行重构得到柴油机振动信号。实际测试结果表明,重构后的信号能反映柴油机机身振动的真实趋势。 相似文献
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Planetary gearbox fault diagnosis under nonstationary conditions is important for many engineering applications in general and for wind turbines in particular because of their time-varying operating conditions. This paper focuses on the identification of time-varying characteristic frequencies from complex nonstationary vibration signals for fault diagnosis of wind turbines under nonstationary conditions. We propose a time–frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms. The Vold-Kalman filter is improved such that it is encoders/tachometers-free. It can decompose an arbitrarily complex signal into mono-components without resorting to speed inputs, thus satisfying the mono-component requirement by the HOES algorithm. The HOES is then used to accurately estimate the instantaneous frequency because of its high adaptability to local signal changes. The derived time–frequency distribution features fine resolution without cross-term interferences and thus facilitates extracting time-varying frequency components from highly complex and nonstationary signals. The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions. The results have shown that the method is effective in detecting both distributed (wear on every tooth) and localized (chipping on one tooth) gear faults. 相似文献
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Tsoumas I.P. Georgoulas G. Mitronikas E.D. Safacas A.N. 《Energy Conversion, IEEE Transaction on》2008,23(2):444-459
This paper introduces a novel approach for the detection of rotor faults in asynchronous machines, based on wavelet analysis of the stator phase current. To be more specific, the measured stator phase current is filtered through a complex wavelet. Theoretical analysis validates that the spectrum of the modulus of the result of the filtering is free from the fundamental supply frequency component, and the fault characteristics can be highlighted. This is advantageous, especially if the induction machine operates at low slip values, where the characteristic frequency components of the rotor fault are very close to the fundamental frequency component. At the same time, by matching the wavelet function to the frequencies of the faulty components, a narrow bandpass filter at the frequency region of the fault characteristic spectral components is obtained. Furthermore, in the context of this paper, features extracted using the proposed technique are used as input to a support vector machine classifier that is employed for the detection of the rotor fault. Simulation and experimental results demonstrate the effectiveness of the proposed technique. 相似文献
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针对经验小波变换(Empirical Wavelet Transform,EWT)对强噪声环境下风力机齿轮箱轴承轻微故障特征提取不足的问题,利用滑移窗口提取子带的连续平均谱负熵(Continuous Average Spectral Negentropy,CASN)对EWT算法进行改进。通过CASN-EWT方法分解风力机齿轮箱轴承故障信号,采用峭度准则对所得分量进行筛选并重构,再开展包络分析,准确提取出故障特征。结果表明:CASN-EWT方法在保留EWT算法自适应性和有效避免模态混叠效应与端点效应优点的同时,能够极大提高EWT分解算法对噪声影响的鲁棒性,有利于准确提取故障特征频率,实现故障精确识别。 相似文献
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基于气缸盖振动信号的柴油机故障诊断研究 总被引:2,自引:0,他引:2
在3110柴油机的1号缸上进行了气门间隙变化、断油等故障的模拟试验研究.分别在正常情况及各故障情况下测取了缸盖振动信号,对信号进行时域分段,分别用时域、频域及小波分析方法对信号进行了分析,提出了故障时频特征的提取方法,并用模糊聚类方法进行了分类分析.对正常情况(气门间隙0.3 mm)、断油故障、气门间隙过小(0.1 mm) 、气门间隙过大(0.9 mm)等4种情况的80组数据的分析结果表明,大部分数据分类效果较理想,错误率仅为6.25 %, 断油故障数据分类完全正确.对进气门间隙为0.1 mm、0.3 mm、0.9 mm的3种工况的60组数据进行了模糊聚类分析,取进气门落座信号,用绝对值均值、有效值及峰值作为特征值,分类结果完全正确. 相似文献
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利用振动信号的小波变换识别内燃机噪声源的研究 总被引:7,自引:2,他引:7
在某车用发动机的不同部件上采集了振动速度信号,利用连续小波变换的方法对信号进行了研究.根据小波变换的时频分析特点,从小波变换幅值的时频图上提取出振动信号的特征,从中识别出不同的部件对发动机表面辐射噪声的不同频率成分的贡献.小波变换方法得到的结果与声强模态方法得到的结果相比有较好的一致性. 相似文献