转子压缩机摩擦噪声机理探究

为了研究转子压缩机摩擦噪声的形成机理,基于摩擦耦合引起摩擦系统自激振动的理论,建立了全尺寸的转子式空调压缩机摩擦噪声的有限元模型,模型中的接触对有:曲轴与上下法兰接触形成主副轴承,曲轴偏心部与滚子接触形成轴颈轴承,曲轴下偏心部端面与下法兰接触形成止推轴承.定义各个接触对的接触属性为库伦摩擦.利用复特征值分析法研究了该系...     

汽车刹车装置摩擦噪声的试验研究

本文对刹车时发出的尖叫摩擦噪声的机理进行了研究。通过对不同工况下摩擦噪声及振动的测量和频谱分析，以及对刹车装置动力学模型的分析与研究表明：尖叫摩擦噪声是由摩擦引发的高频自激振动所致。同时还探讨了这种噪声产生的原因及不同工作条件对其影响的规律。     

某车车内噪声信号的测试与分析

依据国标规定,对某车车内噪声进行测试。利用傅立叶变换对在不同转速工况下测得的噪声信号进行频谱分析,得到其关键频率。在对急加速工况分析的过程中发现虽然傅立叶变换可以分析出整体的变化规律,但不能得到其细节信息。由此采用小波分析对其进行补充,利用小波变换的“自适应变化”的时频窗结构得到信号的细节,并加以处理得到更多的频率信息。     

干扰噪声中获取有益信号的研究

把传模拟的干扰噪声与理想信号进行迭加后，使信号带有明显的噪声。通过带波滤波器后，滤掉了噪声，从而得到了有用信号。     

分形噪声中谐波信号的提取

本文提出一种分形噪声中谐波信号的提取算法。它利用小波变换对1／f噪声的白化作用,结合自适应自调谐滤波器组,在小波域实现了分形噪声与谐波信号的分离。仿真结果表明,该算法能够有效地抑制分形噪声,显著地提高信噪比。     

时滞摩擦尖叫噪声模型的稳定性研究

应用Olgac直接法对单自由度时滞摩擦噪声模型的稳定性进行了分析,建立了无摩擦尖叫噪声的稳定区域.对模型的自然频率、阻尼和接触摩擦综合系数对摩擦尖叫噪声的影响进行了数值分析.结果显示,摩擦力时滞的数值对摩擦噪声的发生有重要的影响.随着摩擦力时滞数值的增大,模型无摩擦噪声的稳定区和有摩擦噪声的不稳定区交替出现.模型的自然频率越高,就越容易发生摩擦噪声.模型的阻尼越小,也越容易发生摩擦噪声.模型的接触摩擦综合系数越大,也越容易发生摩擦噪声.对模型进行了非线性仿真分析,仿真结果显示,法向振动引起的接触分离是摩擦振动受限的一个非线性因素.     

音响系统中的噪声信号

噪声（通俗地叫噪音）是最常遇到的音响系统问题之一,无论是在录音制作还是扩音演出等活动中,噪声无处不在,如何尽量减小和避免噪声的干扰,认识噪声的来源和解决方法又有哪些呢？     

高速工况下乘员耳侧噪声信号重构方法

高速工况下,车内噪声信号具有随机性和波动性的特征。将一种基于经验模态分解(EmpiricalModeDecomposition, EMD)和反向传输(Back Propagation, BP)神经网络的算法,用于重构车内乘员耳侧噪声信号。首先通过对车内乘员耳侧噪声贡献量分析,确定关键噪声源信号;其次对选择的噪声源信号进行EMD分解,得到有限个相对平稳的固有模态函数(Intrinsic Mode Function, IMF)分量;然后采用极值点划分法,按各个分量的波动情况进行重新划分,将信号分量重构为高频、中频和低频3个分量;最后对不同频段的部分建立相应BP神经网络模型,并将不同频段分量的重构结果叠加作为原信号的重构结果。以在某轿车采集到的5个噪声信号源为基础,利用该方法进行乘员耳侧噪声信号重构,并对其进行分析。结果表明:提出的噪声重构方法可以实现高速工况乘员耳侧噪声信号的重构,并具有良好的性能。     

基于盲信号处理的机械噪声监测与故障诊断

摘　要: 在介绍盲信号处理统一模型的基础上,全面归纳了其典型算法,综述了它在机械噪声监测与故障诊断中的研究现状,重点讨论了混合模型、噪声盲源分离与盲解卷积、盲源分离与多种技术的结合、噪声监测与诊断系统研究等方面的应用,并指出需要进一步研究的主要问题。
     

消除图像脉冲噪声的模糊结合滤波器

提出模糊结合滤波用于消除脉冲噪声方法。它由噪声率检测、噪声污染程度W评价和模糊结合滤波器组成。根据选择中值滤波或最大最小排除均值滤波。由W确定模糊隶属函数及模糊判决规则。建立了模糊结合滤波的数学模型。模拟实验表明,椒盐噪声概率为80%时,滤波输出的峰值信噪比为25.8dB,均方误差为171,而且能很好地保护图像细节。     

Signal Processing for Remote Condition Monitoring of Railway Points

Abstract:  The dependability of railway points (turnouts or switches) is a key part of any railway system; the Potters Bar crash (10 May 2002) in the UK, which led to seven fatalities is a key example of a failure of this subsystem. Present maintenance of points involves overly frequent inspection by maintenance staff. A remote condition monitoring approach would lead to more efficient inspection routines and directed anticipatory maintenance trips. To assist the creation of a suitable fault-detection algorithm, the authors analysed existing force and current data for the 'as commissioned' case of a turnout and for situations with different fault conditions. Signal processing of this data revealed several different methods that can be used to distinguish between fully functioning points, and different fault conditions of the points. Specifically, clustering of statistical parameters and their application to wavelet levels and coefficients, provided clear discrimination of most critical faults. This demonstrates a good first step towards a condition monitoring-based maintenance regime for points that is both safer for passengers and maintenance personnel and that has the potential to be more effective and economical.     

CIS信号检测与处理技术

ＣＩＳ（接触式图象传感器）是一种新型图象传感器。由于ＣＩＳ头比ＣＣＤ部件更紧凑,因此可使的产品体积更小。阐述了ＣＩＳ的特点,基本结构和信号处理技术,另外,还介绍了自选研制的ＣＩＳ信号处理系统及ＣＩＳ的信号实侧波形。     

Analysis of Barkhausen noise using wavelet-based fractal signal processing for fatigue crack detection

The paper presents new approach to Barkhausen noise signal processing for detection of fatigue crack. Barkhausen noise signal from mild steel samples under axial fatigue is investigated using fractal signal processing, particularly wavelet variance method. Based on repeatability analysis new algorithm is developed and applied to acquired signals. The influence of fatigue on fractal characteristics of Barkhausen noise is analyzed. Signal analysis reveals significant and repeatable changes in wavelet variance, spectral parameter and estimated Hurst exponent just after crack initiation. The results demonstrate high potential of fractal analysis of Surface Barkhausen noise applied to fatigue crack initiation detection.     

分离谱技术在超声衍射时差法信号处理中的应用

超声衍射时差（TOFD）方法因具有普通超声检测和射线检测的优点而被广泛应用于中厚板的焊缝检测与缺陷定量中。TOFD技术检测的是相对微弱、指向性差的衍射波信号,被检测材料所产生的结构噪声也会降低检测信号的信噪比,影响了TOFD检测的精度。将分离谱技术用于超声TOFD检测信号的处理,采用线性平均、极值阈值、极值阈值＋最小值、最小值等四种恢复算法进行比较,并在最小值算法的基础上引入最小值选中次数加权算法恢复信号。结果表明：与传统的滤波方法相比,该方法能有效地提高了回波信号的信噪比,减小了TOFD检测中的缺陷定量误差。     

光纤准白光干涉信号数字化处理方法

提出一种光纤准白光干涉信号数字化处理方法。该方法采用干涉条纹脉冲实现干涉包络信 号同步采集,克服了导轨移动速度不均匀造成的影响;通过高斯函数曲线拟合方法高精度确定干涉信号零光程差点;大大提高了准白光干涉信号定位精度。为消除包络信号采样边界点不确定性带来的误差,采用外插法进行补偿。实验表明,采用该方法对光纤准白光干涉信号零光程差位置的定位精度优于0.5个干涉条纹。     

Optimal reduction of noise in image processing using collaborative inpainting filtering with Pillar K-Mean clustering

Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is characterized as an unwanted disturbance which occurs while capturing the actual image thus affecting the quality of the image. Hence, noise formation is considered as a perilous issue and the reduction of noise is considered as an awkward process. Nowadays, almost in all fields of science and technology, digital image processing is increasing rapidly, so there arises the need for de-noising to cure the noised image. The main objective of this paper is to overcome the issue of noise and also to increase the quality and pixel value of the image. An advanced methodology known as collaborative filtering and Pillar K-Mean clustering is discussed in this paper to overcome the abovementioned problem. Initially, distinct pure images are taken as the dataset and three types of noises are added to the corresponding image to make it as a noised one. Hence, the unspecified noise is resolved on the basis of a hybrid combination of algorithms of collaborative filtering with the image inpainting method. Sequentially, the low-density noises, such as random noise and poison noise, are recovered by the implementation of collaborative filtering, and the high-density salt and pepper noise are recovered by the image inpainting method. Based on the GLCM (Grey Level Co-occurrence Matrix) feature, the normal image and the noised image are used for the clustering process. Then the de-noised image is evaluated to find the efficiency on the basis of few parameters such as SNR (Signal to Noise Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SSI (Structural Similarity Index). Accordingly, the evaluated images are further withstood for clustering to differentiate the noises by applying the proposed clustering methodology. Then the evaluated images are verified on the basis of a few parameters such as Silhouette Width, Davies–Bouldin Index and Dunn Index. The proposed methodology is run on the platform of Mat Lab. Finally, the proposed methodology is considered as an efficient method for settling the issue in digital image de-noising.     

纳米测量中的光栅信号细分技术

文章介绍了一种高精度位移测量方法,以LabVIEW作为工具,对高精度线性衍射光栅干涉仪(LDGI)的正交弦波信号进行采集、处理和分析,进行准确、快速的计数和细分,计算出精确的位移,并进行方向判断.以软件进行信号处理、位移计算,并对可能存在的信号异常进行纠错,简化了硬件,减少了电气环节引入的干扰.实验数据表明:该方法有效地进行高精度位移测量,标准差在20nm以下.