共查询到20条相似文献,搜索用时 15 毫秒
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曲轴轴承早期磨损故障特征微弱且呈现非平稳循环特征,提出一种非平稳循环特征极坐标增强方法。利用连续小波变换对发动机振动信号进行处理,然后根据发动机工作过程与配气相位的关系对于每一工作循环数据进行等角度采样,将信号特征由直角坐标系映射到极坐标系并进行同步增强,并利用模糊C均值聚类对故障特征参数进行分类识别。仿真信号的分析对比显示了该方法能够削弱噪声干扰,突出信号特征。运用此方法对某型发动机曲轴轴承磨损信号进行分析,有效地提取了曲轴轴承磨损特征信息,准确识别了曲轴轴承不同磨损故障。 相似文献
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针对变速齿轮箱中复合故障的故障特征提取,提出了一种基于阶次解调谱的变速齿轮箱复合故障诊断方法。变速齿轮箱中的转速具有时变的特性,而故障特征往往与转速相关,亦具有时变特性。本文方法先用线调频小波路径追踪算法从原始振动信号中提取转频曲线,再根据转频曲线对原始振动信号进行等角度重采样,将时域非平稳信号转化为角域周期平稳信号,最后对角域周期平稳信号进行能量算子解调分析,根据阶次解调谱中的调制信息进行变速齿轮箱复合故障诊断。通过算法仿真和应用实例对包含齿轮局部故障和轴承局部故障的变速齿轮箱复合故障进行了分析,结果表明,本文方法在无转速计的情况下能有效地提取变速齿轮箱复合故障的故障特征。 相似文献
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A parametric time-frequency representation is presented based on time-varying autoregressive model (TVAR), followed by applications
to non-stationary vibration signal processing. The identification of time-varying model coefficients and the determination
of model order, are addressed by means of neural networks and genetic algorithms, respectively. Firstly, a simulated signal
which mimic the rotor vibration during run-up stages was processed for a comparative study on TVAR and other non-parametric
time-frequency representations such as Short Time Fourier Transform, Continuous Wavelet Transform, Empirical Mode Decomposition,
Wigner-Ville Distribution and Choi-Williams Distribution, in terms of their resolutions, accuracy, cross term suppression
as well as noise resistance. Secondly, TVAR was applied to analyse non-stationary vibration signals collected from a rotor
test rig during run-up stages, with an aim to extract fault symptoms under non-stationary operating conditions. Simulation
and experimental results demonstrate that TVAR is an effective solution to non-stationary signal analysis and has strong capability
in signal time-frequency feature extraction. 相似文献
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Representation of nonstationary stochastic excitations is crucial for stochastic response analyses of (time-varying) linear and nonlinear structural systems. This paper proposes a new representation method of non-stationary stochastic excitations based on the generalized harmonic wavelet (GHW) that takes the phase angles and frequencies as basic random variables. The orthogonal properties of the discrete-form spectral process increments describing non-stationary stochastic processes are formulated. Then the GHW-based representation is derived by using the orthogonal properties. This method can be used to accurately reproduce non-stationary stochastic excitations with the target asymptotic Gaussianity and evolutionary power spectrum density. The effectiveness and accuracy of the proposed method have been validated via numerical examples. This study provides a novel way for the representation of non-stationary processes and deserves to be applied in the stochastic response analyses of structures. 相似文献
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前后向时间序列模型联合估计的时变结构模态参数辨识 总被引:1,自引:0,他引:1
为提高时变结构模态参数辨识精度和抗噪声能力,提出一种前后向泛函向量时变自回归滑动平均(FS-VTARMA)时间序列模型联合估计的模态参数辨识方法。首先建立前后向FS-VTARMA模型联合估计的均方误差形式的费用函数,其次引入非平稳信号中前向模型和后向模型估计系数的近似共轭关系,再利用两步最小二乘法(2SLS)得到时变模型系数,最后把时变模型特征方程转换为广义特征值问题提取出模态参数。利用时变刚度系统非平稳振动信号验证该方法,结果表明:能有效地克服前向模型估计中模态参数一步延迟以及起始时刻无法准确获得,以及后向模型估计中模态参数一步超前以及终止时刻无法准确获得的缺点,具有更高的模态参数辨识精度和更强的抗噪声能力。 相似文献
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This paper proposes a non-stationary random response analysis method of structures with uncertain parameters. The structural physical parameters and the input parameters are considered as random variables or interval variables. By using the pseudo-excitation method and the direct differentiation method (DDM), the analytical expression of the time-varying power spectrum and the time-varying variance of the structure response can be obtained in the framework of first order perturbation approaches. In addition, the analytical expression of the first-order and second-order partial derivative (e.g., time-varying sensitivity coefficient) for the time-varying power spectrum and the time-varying variance of the structure response expressed via the uncertainty parameters can also be determined. Based on this and the perturbation technique, the probabilistic and non-probabilistic analysis methods to calculate the upper and lower bounds of the time-varying variance of the structure response are proposed. Finally the effectiveness of the proposed method is demonstrated by numerical examples compared with the Monte Carlo solutions and the vertex solutions. 相似文献
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Mainardi LT 《Philosophical transactions. Series A, Mathematical, physical, and engineering sciences》2009,367(1887):255-275
In the last decades, one of the main challenges in the study of heart rate variability (HRV) signals has been the quantification of the low-frequency (LF) and high-frequency (HF) components of the HRV spectrum during non-stationary events. At this regard, different time-frequency and time-varying approaches have been proposed with the aim to track the modification of the HRV spectra during ischaemic attacks, provocative stress testing, sleep or daily-life activities. The quantitative evaluation of power (and frequencies) of the LF and HF components has been approached in various ways depending on the selected time-frequency method. This paper is an excursus through the most common time-frequency/time-varying representation of the HRV signal with a special emphasis on the algorithms employed for the reliable quantification of the LF and HF parameters and their tracking. 相似文献
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基于小波降噪和短时傅里叶变换的主轴突加不平衡非平稳信号分析 总被引:1,自引:0,他引:1
针对主轴运行过程中突加不平衡而产生的非平稳信号无法采用传统的傅里叶变换对信号进行分析处理的问题,提出一种采用小波降噪与短时傅里叶变换相结合对主轴振动特征信息进行准确提取的方法,该方法利用小波降噪技术对非平稳信号进行滤波处理,再对滤波处理后的信号进行重构,最后通过短时傅里叶变换精确获取主轴振动幅值,通过仿真和实验验证了该方法的有效性。 相似文献
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针对遥测振动信号非线性、非平稳性、瞬态冲击性等特点,提出一种基于时频流形自适应稀疏重构的遥测振动信号特征增强方法,对振动信号进行相空间重构提取其时频流形;以时频流形为基础,采用KSVD算法自适应构建过完备字典,并从中找到最匹配的时频原子,根据得到的原子与相空间展开信号的时频分布,依次匹配计算获得其重构的稀疏系数;利用稀疏系数和时频原子对相空间中各维信号的时频分布进行重构,通过时频分布的逆运算和相空间还原得到特征增强信号。仿真和实测信号处理结果验证了算法的有效性。 相似文献
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Dakovic M. Thayaparan T. Djukanovic S. Stankovic L.J. 《Radar, Sonar & Navigation, IET》2008,2(4):306-314
The problem addressed here is non-stationary interference suppression in noise radar systems. Towards this aim, two linear time ?frequency (TF) transforms, short-time Fourier transform and local polynomial Fourier transform are used as a means of signal representation and filtering. The noise radar return signal is a wideband random signal occupying the whole TF plane, whereas the interference signal is well concentrated in the TF plane. This implies that the filtering of the received signal can be performed by using a binary mask to excise only a portion of the TF plane corrupted by the interference. Simulations carried out on the radar return signal corrupted by extremely strong non-stationary interferences confirm the effectiveness of the proposed method. 相似文献
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作为一种新的非平稳信号处理方法,固有时间尺度分解法在将复杂的非平稳信号分解为若干个固有旋转分量过程中,存在着严重的边界效应问题。本文针对抑制边界效应提出了五种数据延拓方法:自适应波形匹配、基于AR模型的延拓方法、镜像延拓方法、多项式延拓方法、反对称周期延拓法。通过数学模拟实验比较这五种方法的抑制效果,选出最优方法。将最优方法用于仿真信号和轴承故障振动信号分析,结果表明该方法能够有效地抑制边界效应,可更好地提取机电设备故障特征。. 相似文献
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RONG Ming-xing 《国际设备工程与管理》2012,(2):104-111
In the motor fault diagnosis technique,vibration and stator current frequency components of detection are two main means.This article will discuss the signal detection method based on vibration fault.Because the motor vibration signal is a non-stationary random signal,fault signals often contain a lot of time-varying,burst properties of ingredients.The traditional Fourier signal analysis can not effectively extract the motor fault characteristics,but are also likely to be rich in failure information but a weak signal as noise.Therefore,we introduce wavelet packet transforms to extract the fault characteristics of the signal information.Obtained was the result as the neural network input signal,using the L-M neural network optimization method for training,and then used the BP network for fault recognition.This paper uses Matlab software to simulate and confirmed the method of motor fault diagnosis validity and accuracy. 相似文献
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针对强噪声条件下滚动轴承故障冲击特征难以提取的特点,提出了一种基于傅里叶分解与奇异值差分谱的滚动轴承故障诊断方法。首先通过傅里叶分解将非平稳的原始轴承故障振动信号分解为若干个固有频带函数,然后运用互相关系数法筛选固有频带函数进行信号重构,并对重构后的信号进行奇异值差分谱降噪,最后对联合降噪后的信号进行Hilbert包络谱分析,准确地识别出故障特征频率,进行故障诊断。仿真分析和试验都很好地验证了该方法的有效性。 相似文献
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利用HHT方法对非平稳风力的时频分析 总被引:2,自引:1,他引:1
非平稳信号的分析方法是信号分析领域中的一个重要问题。本文以风洞试验获得的非平稳风压信号和升力系数信号为研究对象,采用HHT方法对信号进行时频分析。HHT(Hilbert-Huang transform)方法可以获得有意义的瞬时频率,从而给出频率随时间变化的精确表达;信号最终被表示为时频平面上的能量分布,成为Hilbert谱;该方法适用于分析生活中普遍存在的大量频率随时间变化的非线性、非平稳信号,可将复杂的信号直接分离成从高频到低频的若干阶固有模态函数。分析结果虽然没有表现出明显的频谱分布特性,但与以往HHT分析结果提取的固有模态函数不同,如果对本试验获得的非平稳信号提取的固有模态是低频部分的残余信号,忽略其他高频信号,则风压时程和升力系数时程的残余信号曲线就可以分别回归为一个线性函数和一个正弦函数。这也说明,该非平稳信号的主成分仍然是由平稳信号组成的,可以用分析平稳信号的方法进行时程分析。 相似文献
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端点检测技术是语音信号处理的关键技术之一,为提高低信噪比环境下端点检测的准确率和稳健性,提出了一种非平稳噪声抑制和调制域谱减结合功率归一化倒谱距离的端点检测算法。该算法首先通过抑制非平稳噪声再采用调制域谱减消除残余噪声来提升信噪比,减少语音失真。然后再提取每帧信号的功率归一化倒谱系数,计算每帧信号与背景噪声的功率归一化倒谱距离。最后将该倒谱距离作为检测参数,采用双门限判决方法进行端点检测。实验结果表明,该端点检测算法对语音帧和噪声帧具有较好的区分性。此外,在低信噪比环境下,所提出的算法对于不同类型的噪声都具有较好的稳健性。 相似文献