小波分析用于机械系统突发信号在线检测

近年来引起各领域广泛关注的小波分析理论,以其良好的时频局部化功能提供了一种瞬态信号的分析方法,但单纯基于此算法的软件往往缺少对于突发性信号的自适应能力。神经网络则具有良好的自适应性、自组织性及很强的学习功能。本文将此二者结合,以神经网络原理和现代数学小波分析为依据,提出了基于神经网络思想的小波分析;改进原有算法,以自编C程序识别工程技术测量中遇到的突发故障与噪声,并在强噪声环境中机械系统的突发信号实测中获得成功。     

Application of the continuous wavelet transform in periodic error compensation

This paper introduces a new discrete time continuous wavelet transform (DTCWT)-based algorithm, which can be implemented in real time to quantify and compensate periodic error for constant and non-constant velocity motion in heterodyne displacement measuring interferometry. It identifies the periodic error by measuring the phase and amplitude information at different orders (the periodic error is modeled as a summation of pure sine signals), reconstructs the periodic error by combining the magnitudes for all orders, and compensates the periodic error by subtracting the reconstructed error from the displacement signal measured by the interferometer. The algorithm is validated by comparing the compensated results with a traditional frequency domain approach for constant velocity motion. The algorithm demonstrates successful reduction of the first order periodic error amplitude from 4 nm to 0.24 nm (a 94% decrease) and a reduction of the second order periodic error from 2.5 nm to 0.3 nm (an 88% decrease). The algorithm also reduces periodic errors for non-constant velocity motion overcoming limitations of existing methods.     

Damage identification by wavelet analysis

Structural health monitoring (SHM) has been related to damage monitoring with operational loads playing a significant role in terms of fatigue life and damage accumulation prognostics. A lot of different techniques like acoustic emission, ultrasonic, acousto-ultrasonic, guided ultrasonic waves or Lamb waves are nowadays investigated in terms of efficient and user-friendly damage identification system. Every damage identification system available consists of the hardware and software. This paper deals with the latter which is based on propagating Lamb wave measurements. It has been developed especially for distinguishing different kinds of damages. The reason for that research is that for example small voids in material, classified as damage, do not influence its overall strength. The literature gives enormous number of application of wavelets and Lamb waves but only for detecting the damage. Distinguishing seems not to be a subject of wide interest. The usage of wavelet transformation with propagating Lamb waves for distinguishing between different failures is the most important novelty of the research done. To obtain the presented results for modelling, the FFT-based spectral element method has been used. For signal processing, the wavelet analysis has been employed. The proper results are given and future of this research direction is discussed.     

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.     

小波包算法在滚动轴承的在线故障诊断中的应用

对小波变换的理论进行了简要的阐述,并介绍了小波包理论。指出了在强噪声的背景下小波包变换的算法对于瞬态信号提取的有效性,表明了小波包变换对信号的去噪声,滤波等方面具有广泛的前景。并以五套６３０７号轴承为例进行了诊断,结果与实际情况相一致,说明该算法十分适合于滚动轴承的在线监测与故障诊断。     

Fault classification on vibration data with wavelet based feature selection scheme

Fault classification based upon vibration measurements is an essential building block of a conditional based health usage monitoring system. Multiple sensors are incorporated to assure the redundancy and to achieve the desired reliability and accuracy. The shortcoming of using multiple sensors is the need to deal with a high dimensional feature set, a computationally expensive task in classification. It is vital to reduce the feature dimension via an effective feature extraction and feature selection algorithm. A simple wavelet based feature selection scheme is proposed herein, uniquely built by local discriminant bases and genetic optimization. This scheme overcomes the disadvantages faced by the existing feature selection methods by producing a generic feature set, reducing the dimensionality of features, and requiring no prior information of the problem domain. The proposed feature selection scheme is based upon the strategy of "divide and conquer" that significantly reduce the computation time without compromising the classification performance. The simulation results show the proposed feature selection scheme provides at least 65% reduction of the total number of features at no cost of the classification accuracy.     

基于小波变换的齿轮振动信号降噪分析

在齿轮的故障诊断中,振动信号常常夹杂着大量的噪声信号,表现出极大的非平稳性,采用基于小波变换的降噪方法,通过实例对齿轮振动信号进行降噪分析,收到了较好的效果.     

基于小波变换的零件图像数据融合

提出一种基于小波变换的图像数据融合的方法,对多源图像进行分解,将高频区域中的绝对值较大的系数作为重要小波系数;在低频区域,对逼近系数进行加权平均得到新的逼近系数,然后进行小波重构。实验表明,该方法是实用的。     

IMPROVED SINGULAR VALUE DECOMPOSITION TECHNIQUE FOR DETECTING AND EXTRACTING PERIODIC IMPULSE COMPONENT IN A VIBRATION SIGNAL

Vibration acceleration signals are often measured from case surface of a running machine to monitor its condition. If the measured vibration signals display to have periodic impulse components with a certain frequency, there may exist a corresponding local fault in the machine, and if further extracting the periodic impulse components from the vibration signals, the severity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs) of the vibration acceleration signals are often so small that the periodic impulse components are submersed in much background noises and other components, and it is difficult or inconvenient for us to detect and extract the periodic impulse components with the current common analyzing methods for vibration signals. Therefore, another technique, called singular value decomposition (SVD), is tried to be introduced to solve the problem. First, the principle of detecting and extracting the signal periodic components using singular value decompos     

基于小波分析的旋转机械振动信号定量特征研究

通过对机械振动信号的连续小波变换,利用小波滤波器良好的时频特性,研究了振动信号经过连续小波交换后的统计特征。在信号的特征提取中, 引入“灰度矩”并把一阶矩作为定量指标。对8种典型故障信号的研究表明,这种方法能够简单有效地提取信号的特征,区分振动故障。     

运行转子径向振动的小波信号监测

针对运行转子径向振动的信号监测，介绍了小波分析的基本原理，提出了运行转子异常信号检测的原则。利用该原则，本文对实际转子运行的径向振动信号进行了小波信号监测分析，得出了较好的结论。     

Bearing fault detection using wavelet packet transform of induction motor stator current

Induction motor vibrations, caused by bearing defects, result in the modulation of the stator current. In this research, bearing defect is detected using the stator current analysis via Meyer wavelet in the wavelet packet structure, with energy comparison as the fault index. The advantage of this method is in the detection of incipient faults. The presented method is evaluated using experimental signals. Sets of data are gathered before and after using defective bearings. Compared to conventional methods, the superiority of the proposed method is shown in the success of fault detection.     

基于小波包分析的液压泵状态监测方法

液压泵是液压系统中的关键部件,对其运行状态的监测与故障诊断对整个液压系统的可靠性具有重要意义。基于小波包分解和小波系数残差分析方法,提出一种利用液压泵出口压力进行液压泵故障诊断的方法。通过分析液压泵出口处压力信号的特征,利用小波包对压力信号进行频谱分解,提取液压泵的故障特征,建立不同频率范围的特征信号与液压泵不同故障因素的对应关系,为液压泵的故障诊断与定位提供依据。利用小波包能量残差判别液压泵的运行健康状态,并比较不同小波基函数在故障诊断时的敏感度。为减小小波分析时边界效应所引起的信号畸变,引入“滑动双窗口”的分析方法。试验结果表明,与快速傅里叶方法相比,基于小波包分解的残差分析方法可有效提高故障诊断的准确率,实现对液压泵的状态监测与故障诊断。     

小波多重分形及其在振动信号分析中应用的研究

不同于许多基于FFT的信号分析方法,多重分形谱分析的是信号的几何结构特征。以前,多重分形谱的计算方法都有其固有的缺点,使多重分形谱的应用受到限制。而小波局部极大模方法因其简单性和有效性,近来在多重分形谱计算方面受到了广泛的关注。较系统地阐述了多重分形的概念和多重分形谱的小波局部极大模计算方法,讨论了多重分形谱在故障诊断领域的应用途径,并用多重分形谱对不平衡、油膜涡动、联轴器不对中和碰摩等旋转机械的4种典型故障的振动信号进行了事例研究。研究结果表明,多重分形谱能够很好的反映振动信号的几何结构特征,为机械设备故障诊断提供了又一种有效的方法。     

小波分析在汽车发动机机械故障诊断中的应用

文中阐述了汽车发动机机械故障诊断的理论方法,讨论了小波变换的分析方法.由于小波变换具有传统频谱分析方法所没有的时一频分析特征,特别适合于非平稳信号的分析与处理,应用该方法对实测信号进行了有效的时频分析.     

Extraction the unbalance features of spindle system using wavelet transform and power spectral density

Identification of the spindle unbalance is very important for the ultra-precision machining tools. In the first part, the frequency information for the spindle system is computed by two methods. In the second part, the results between the modal information of the spindle and the error frequency of the measured workpiece surface which processed by wavelet transform and power spectral density is compared, the signal feature in the waviness which is consistent with the spindle unbalance frequency is extracted. In the last part, the identified result is compared, it proves the extraction and identification method for the spindle unbalance of the machine tool is correctness and validity.     

基于小波变换和自适应中值滤波的图像去噪

小波图像去噪已经成为目前图像去噪的主要方法之一。该文尝试把小波变换与自适应中值滤波这两种去噪方法相结合,对同时含有高斯噪声和椒盐噪声的图像进行了去噪研究。实验结果表明,此方法在去除噪声的同时也较好地保留了原始图像的边缘信息,效果不仅优于单一的小波变换或普通中值滤波的方法,更优于将小波变换与普通中值滤波相结合的方法。     

Selective noise filtration of image signals using wavelet transform

This paper examines the technique of denoising and signal extraction using wavelet transform scale space decomposition. The noisy signal is decomposed into multiple scales by the dyadic wavelet transform. At a given position, the level of correlation is used to deduce the presence or absence of significant feature of signals or images, which is then passed through the filter. By comparing the correlation information of the data at that scale with those at larger scales, noise is preferentially removed from the data. In the wavelet transform domain, the desired features are identified and retained because they are strongly correlated across scales compared to noise. This denoising algorithm can be used to reduce noise to a high degree of accuracy, while preserving most of the important features of the signal. In this paper, this technique of scale space filtering is applied to sample signals and images. Representative results are presented which shows that considerable noise content in signals and images can be reduced while preserving the value of the desired signal.     

Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance

Machinery vibration signal is a typical multi-component signal and fault features are often submerged by some interference components. To accurately extract fault features, a weak feature enhancement method based on empirical wavelet transform (EWT) and an improved adaptive bistable stochastic resonance (IABSR) is proposed. This method makes full use of the signal decomposition performance of EWT and the signal enhancement of the IABSR to achieve the purpose of fault feature enhancement in low frequency band of FFT spectrum. Firstly, EWT is used as the preprocessing program of bistable stochastic resonance (BSR) to decompose the machinery vibration signal into a set of sub-components. Then, the sensitive component that contains main fault information is further input into BSR system to enhance fault features with the assistance of residual noises. Finally, the fault features are identified from fast Fourier transform (FFT) spectrum of the BSR output. To achieve the optimal BSR output, the IABSR method based on salp swarm algorithm (SSA) is presented. Compared with the tradition adaptive BSR (ABSR), the IABSR optimizes not only the BSR system parameters but also the calculation step size. Two case studies on machinery fault diagnosis demonstrate the effectiveness and superiority of the proposed method. In addition, the proposed method is easy to implement and is robust to noise to some extent.