变速箱振动与噪声分析

分析了某变速箱试验时的异常振动和噪声原因。先对一台样机测试其各挡稳定过程的振动和噪声信号,再对测得的信号进行功率谱密度分析。之后,运用Pro/Engineer建立了变速箱壳体的实体模型,并用OptiStruct软件进行了壳体前端面加零位移约束的模态分析;计算了各挡齿轮的啮合频率,分析了壳体的模态频率与齿轮啮合频率对振动和噪声信号功率谱中峰值的影响。最后根据分析结果,提出对壳体的改进建议,以达到变速箱减振降噪的目的。     

直扩信号PN序列周期盲估计算法的研究

研究了功率谱二次处理法在直扩信号PN序列周期盲估计中的应用,提出了一种基于估计函数矩阵的盲估计算法。该方法充分利用PN序列本身具有良好的相关特性和周期特性,运用矩阵变换和分段累加的方法消除噪声的影响。针对以上两种方法分别进行了Matlab仿真,并论述了两种方法的优缺点和适用范围。     

基于小波相关滤波法的滚动轴承早期故障诊断方法研究

目前基于小波分析的滚动轴承故障诊断方法研究已经很多,但是这些方法对于强噪声背景下的早期故障微弱信号特征提取效果并不理想。为此,提出了适用于强噪声背景的小波相关滤波滚动轴承早期故障诊断方法。该方法将小波相关滤波降噪方法和Hilbert包络细化谱分析相结合:对被测信号进行小波相关滤波降噪处理,对降噪处理后的高频段尺度域的小波系数进行Hilbert包络细化谱分析。该方法在滚动轴承的早期故障诊断中的试验结果表明,该方法与直接小波系数包络谱诊断方法相比,较大地增强了对滚动轴承早期故障诊断的能力,在强噪声背景下有效地提取出滚动轴承的早期故障频率。     

基于信号延拓的采样信号频谱泄漏抑制

提出一种基于信号数据延拓的采样信号离散傅里叶变换频谱泄漏抑制方法,分析了经典谱估计方法中频谱泄漏产生的原因,得出含噪声周期信号的无泄漏条件,进而提出频谱泄漏抑制方法:对采样信号作周期估计和整数周期数据延拓处理,使DFT计算窗内信号的边界连续或近似连续,以达到抑制频谱泄漏的目的。对通信、电力系统中常出现的加性高斯白噪声恶化信号的仿真表明:该方法可以显著减少频谱能量泄漏,获得更高质量的频谱。     

机器故障功率谱诊断的进一步探讨

机器故障往往反映为传感信号功率谱的特征变化,为了在复杂机械工况的在线监视与故障诊断中快速有效地捕捉住功率谱的变化特征,本文进一步探讨了表征信号功率谱特征变化的特征量,谱形扩展系数及其快速算法。与过去的几种典型特征量比较,谱形扩展系数具有适应性强且计算更简便等特点。     

基于奇异值和奇异向量的振动信号降噪方法

针对复杂的转子振动信号中同时存在随机噪声干扰和工频噪声干扰的问题,提出了基于奇异值和奇异向量相结合的降噪方法。首先,对振动信号进行奇异值分解(singular value decomposition,简称SVD),根据奇异值谱确定振动信号有效奇异值阶次;其次,对有效阶次范围内的奇异向量进行快速傅里叶变换(fast Fourier transform,简称FFT),依据幅值谱筛选出对应于工频噪声的奇异向量;最后,利用其余的奇异值和奇异向量进行重构得到降噪的时域信号。通过仿真信号和工程试验信号对该方法进行了验证,结果表明,基于奇异值和奇异向量相结合的降噪方法,不但能有效降低振动信号中的随机噪声干扰,还能有效降低工频噪声干扰,同常用的陷波器方法相比所提出方法具有明显优势。     

应用谱减法处理带噪语音信号的研究

语音信号经常会受到环境噪声的干扰,而噪声会影响语音通信的质量甚至是语音处理系统地运行。对于带噪语音信号的处理,主要目的是提取出尽可能纯净的原始语音。文章研究了基于功率谱的语音处理方法,主要包括功率谱相减算法和改进算法两种;给出了谱减法算法及其改进形式的基本原理、实现方法,并且运用MATLAB软件仿真得出了两种算法的语音处理效果。实验表明,在加性平稳噪声条件下,与常用谱减法算法相比,改进算法能更好地抑制噪声,从而提高了语音质量和信噪比。     

A broadband noise-signal spectrometer of a millimeter wavelength band

The spectrum recording method for the broadband noise signal of millimeter wavelength band is considered. It is based on using resonance frequency meters of the millimeter wavelength band, removing the part of the power from the base waveguide transmission line at the resonance. This frequency meter with a changed design makes it possible, together with a corresponding recording device, to unambiguously define the spectrum width of a broadband noise signal and make a paper copy. An example of spectrum recording on the S4-60 spectrum analyzer screen and a paper copy made by a two-coordinate self-recording instrument is presented.     

Noise analysis and noise reduction methods in kilohertz pump-probe experiments

We analyze sources of noise in kilohertz frequency pump/probe experiments and present a method for reducing experimental noise by identifying and filtering noisy shots. The power spectrum of instrumental noise shows high frequency, small amplitude modulations which cannot be averaged out. A histogram analysis shows that low frequency, large amplitude signals pose a serious obstacle to signal averaging for improved signal to noise. In kilohertz frequency pump/probe experiments, this low frequency noise typically arises from laser scatter due to bubbles, dust, and defects. We quantify the effectiveness in analyzing and rejecting these large amplitude signals which can produce a hindrance to the effectiveness of signal averaging.     

基于多天线接收的频谱感知检测

频谱感知是认知无线电的一个基本的技术,用来检测授权频谱中是否有授权用户的存在。频谱检测是认知无线电中最重要的一个环节。由于噪声的存在,现有的许多方法都或多或少地存在局限性。本文基于信号和噪声在多天线系统中相关性的不同,提出一种新的频谱感知方法。该方法不需要信号和噪声的任何先验知识并且算法构成简单,计算速度快,在低信噪比下表现了良好的性能。     

基于E-SPCM的直线电机动子位置高精度测量方法研究

针对直线电机动子位置测量,引入一种基于扩展采样相位相关法(E-SPCM)的亚像素位移图像检测方法,以提高测量精度和抗干扰性。首先建立了直线电机位置检测系统,通过高速相机实时采集条纹图像序列;其次对条纹图像进行边缘特征提取,利用相位相关得到相邻条纹图像的互功率谱,即动子位置的整像素位移;进而对整像素邻域的互功率谱进行上采样相位相关计算,实现高精度的亚像素位移测量,进一步由系统标定得到实际位移值。对比传统相位相关算法,所采用的方法能够提高测量精度且有很好的噪声抑制性能,最后搭建了实验检测平台,验证了算法的有效性。     

Note: anti-strong-disturbance signal processing method of vortex flowmeter with two sensors

Some digital signal processing methods have been used to deal with the output signal of vortex flowmeter for extracting the flow rate frequency from the noisy output of vortex flow rate sensor and achieving the measurement of small flow rate. In applications, however, the power of noise is larger than that of flow rate sometimes. These strong disturbances are caused by pipe vibration mostly. Under this condition the previous digital signal processing methods will be unavailable. Therefore, an anti-strong-disturbance solution is studied for the vortex flowmeter with two sensors in this Note. In this solution, two piezoelectric sensors are installed in the vortex probe. One is called the flow rate sensor for measuring both the flow rate and vibration noise, and the other is called the vibration sensor for detecting the vibration noise and sensing the flow rate signal weakly at the same time. An anti-strong-disturbance signal processing method combining the frequency-domain substation algorithm with the frequency-variance calculation algorithm is proposed to identify the flow rate frequency. When the peak number of amplitude spectrum of the flow rate sensor is different from that of the vibration sensor, the frequency-domain subtraction algorithm will be adopted; when the peak number of amplitude spectrum of the flow rate sensor is the same as that of the vibration sensor, the frequency-variance calculation algorithm will be employed. The whole algorithm is implemented in real time by an ultralow power micro control unit (MCU) to meet requirements of process instrumentation. The experimental results show that this method can obtain the flow rate frequency correctly even if the power of the pipe vibration noise is larger than that of the vortex flow rate signal.     

Frequency-domain computation of inflow broadband noise due to interaction of a rectilinear cascade of flat plates with incident turbulence

This paper deals with the broadband noise due to the interaction between convected turbulent gusts and a rectilinear cascade of flat plates bounded by two parallel walls. An analytic formulation for the acoustic power spectrum due to this turbulence-cascade interaction is derived, which can be used to assess the effects of the span-wise wavenumber components of ingesting turbulent gust on the overall acoustic power spectrum. This three-dimensional theory is based on the two-dimensional theory of Cheong et al. (2006, 2009). The three-dimensional model is shown to provide a close fit to the measured spectrum of rotor-stator interaction. The predictions using this three-dimensional model are also compared with those using the previous two-dimensional model by Cheong et al. (2006; 2009). Through this comparison, it is found that the contributions to the acoustic power of the span-wise wavenumber components of incident turbulent gust are increased as the frequency is augmented, which is mainly due to three-dimensional dispersion-relation characteristics of acoustic waves. This implies that the number of incident turbulent gust modes directly involved in generating cut-on acoustic waves increases as the frequency increases. Therefore, in the lower frequency range, three-dimensional acoustic power is less than its corresponding two-dimensional one, whereas, as the frequency increases, the three-dimensional acoustic power spectrum closely follows those of two-dimension. The formulation is also used to make a parametric study about the effects on the power spectrum of the blade number, stagger angle, gap-chord ratio, and Mach number.     

液力变速箱噪声源识别研究

液力变速箱是大吨位内燃叉车的重要动力传递机构,其噪声性能是国产大吨位内燃叉车产品质量的主要因素之一。为查出液力变速箱的主要噪声源,设计和搭建液力变速箱试验台,利用阶比跟踪分析技术,在试验台上对液力变速箱不同工况下的振动噪声信号进行采集,运用亿恒分析软件和编制好的阶比跟踪谱、三维转速谱阵程序对液力变速箱的振动、噪声信号进行分析。得出噪声源是全速前进二挡时候,齿轮Z9引起的1463．38Hz峰值噪声与液力变速箱固有频率1500Hz的峰值噪声产生峰值叠加,造成噪声过大。     

Spurious pulse rejection in a photon correlation experiment

A method has been developed to significantly suppress the effect of spurious pulses on the correlation spectrum in photon correlation experiments. When the correlation experiment is operated at a low count rate (<2 true pulses per total measured correlation time), the circuit rejects an incoming pulse stream whenever more than two pulses are detected in that interval. Details of this spurious noise pulse rejection circuit are presented. This circuit has been incorporated into an existing correlation computer system as an option selected by the operator. Application of this circuit to a test case where the spurious pulse correlations were excessive compared with the signal events yielded very accurate signal correlation spectra.     

基于相关自适应器的EM-MWD信号检测系统设计

经EM-MWD(electromagnetic method measurement while drilling)电磁通道传输至地表的信号很微弱并且极易受到白噪声、奇异噪声、工频噪声及其谐波等干扰,导致信号特征参数提取的准确度降低,为了解决这一难题,通过对电磁波传输信道的研究,根据接收初始信号强度以及自适应检测和相关检测的特点,研究并设计了相关自适应器,并基于此设计了电磁随钻地表信号检测系统。然后用Hilbert变换求信号包络,完成了数据拟合和残差分析,并计算了信噪比、均方根误差和误码率,最后做了实验。仿真和实验结果表明,利用该检测系统,能够提高信号特征参数的准确度,达到有效降噪目的,对后续分析和研究提供了保证。     

Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is very simple for operator to identify the bearing fault type. Moreover, the proposed method can be conducted in an almost automatic way. The results obtained from simulated and practical experiments prove that the proposed method is very effective for bearing faults diagnosis.     

A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery

In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal–noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.     

Precision of light intensity measurement in biological optical microscopy

Standardization and calibration of optical microscopy systems have become an important issue owing to the increasing role of biological imaging in high‐content screening technology. The proper interpretation of data from high‐content screening imaging experiments requires detailed information about the capabilities of the systems, including their available dynamic range, sensitivity and noise. Currently available techniques for calibration and standardization of digital microscopes commonly used in cell biology laboratories provide an estimation of stability and measurement precision (noise) of an imaging system at a single level of signal intensity. In addition, only the total noise level, not its characteristics (spectrum), is measured. We propose a novel technique for estimation of temporal variability of signal and noise in microscopic imaging. The method requires registration of a time series of images of any stationary biological specimen. The subsequent analysis involves a multi‐step process, which separates monotonic, periodic and random components of every pixel intensity change in time. The technique allows simultaneous determination of dark, photonic and multiplicative components of noise present in biological measurements. Consequently, a respective confidence interval (noise level) is obtained for each level of signal. The technique is validated using test sets of biological images with known signal and noise characteristics. The method is also applied to assess uncertainty of measurement obtained with two CCD cameras in a wide‐field microscope.     

电动机轴承故障诊断的循环平稳方法

通过信号仿真,研究了二阶循环平稳分析的解调性能,并总结出循环自相关与谱相关切片函数的一般规律,分析了故障电动机轴承振动信号的循环平稳特性。试验中,模拟并采集工况中带有强噪声的内圈故障电动机轴承的振动信号,提出用小波消噪与特殊频率处谱相关切片结合的分析方法,进行特征提取,分析过程中发现,这种方法能有效实现轴承快速诊断,避免三维函数分析信息冗余、计算量庞大的缺点,同时也排除了瞬时转速与额定转速偏差引起的干扰。