Gear fault identification and localization using analytic wavelet transform of vibration signal

The aim of this present work is to identify and localize the defect in gear and measure the angle between two damaged teeth in the time domain of the vibration signal. The vibration signals are captured from the experiments and the burst in the vibration signal is focused in the analysis. The enveloping technique is revisited for defect identification but is found unsatisfactory in measuring the angle between two faulty teeth. A signal processing scheme is proposed to filter the noise and to measure the angle between two damaged teeth. The proposed technique consists of undecimated wavelet transform (UWT), which is used to denoise the signal. The analytic wavelet transform (AWT) has been implemented on approximation signal followed by a time marginal integration (TMI) of the AWT scalogram. The TMI graph time-axis is mapped onto the angular displacement of the driver gear. The measurement is shown to identify the first and the second defective teeth impact on gear meshing, which is visible as sharp spikes in the TMI graph. An attempt is also made to replace the approximation from UWT with Intrinsic Mode Function (IMF) derived from the Empirical Mode Decomposition (EMD). The present experimental work establishes the proposed method of measuring and localizing multiple gear teeth defect using vibration signal in the time domain.     

Enhancing Weak Defect Features Using Undecimated and Adaptive Wavelet Transform for Estimation of Roller Defect Size in a Bearing

Defects in bearings affect the vibration level, resulting in and increase in temperature and decomposition of lubricant. Estimation of roller defect size is a complex task because it revolves as well as rotates during the motion. Signals from a defective roller of a bearing are superimposed by the signal from races, cage, and background noise. In this communication, a signal processing scheme is proposed that makes the signal suitable for estimating the size of the defect in the rolling element of a tapered roller bearing. To achieve this, in the first stage of processing, shift-invariant soft thresholding is applied to denoise the signal. It suppresses the noise without affecting defect-related features. Further, in the second stage of processing, continuous wavelet transform (CWT) using adaptive wavelet is applied. The adaptive wavelet is designed from the impulse extracted from the signal using the least squares fitting method. It results in higher coefficients in the region of impulse produced due to the defect. Finally, time marginal integration (TMI) of CWT coefficients is carried out for estimation of defect width. A study was performed for six different cases in which the size of the defect and orientation varies. Results of measurements of roller defect widths estimated using the proposed scheme were compared with defect widths calculated using image examination. For the nonoverlapping signature of defects (such as defects at 0° and 90° orientations), the maximum deviation in the width measurement using the proposed scheme is 6.52%. The error may increase when signature of two defects are overlapped.     

基于自适应最大相关峭度解卷积和频率切片小波变换的齿轮故障特征提取

针对最大相关峭度解卷积(MCKD)降噪效果受滤波器阶数影响的问题,提出了自适应MCKD方法。针对频率切片小波变换(FSWT)在强背景噪声中提取冲击故障特征的不足,提出了自适应MCKD和FSWT相结合的齿轮故障特征提取方法。首先用自适应MCKD对噪声齿轮信号进行降噪处理,然后对降噪后的信号进行频率切片小波变换和故障特征提取。齿轮故障诊断实例的分析结果验证了该方法的有效性。     

基于复小波变换相位谱的齿轮故障诊断

提出了一种基于复小波变换诊断齿轮故障的新方法。利用Mexican-hat调制复小波基函数对齿轮振动信号进行连续小波变换，再作相位的频谱分析，可以突出边频带结构，识别不同故障模式。试验数据的分析结果表明，该方法适用于齿轮故障诊断，与传统的自功率谱方法以及基于实值小波的小波变换方法相比，这种方法效果更好。     

小波变换的流体压力信号自适应滤波方法研究

为了有效地消除流体压力信号中的噪声,提出了一种基于小波变换的自适应滤波算法,该算法针对信号和噪声经小波变换后在不同尺度上的特征不同,先对信号进行小波多尺度分解,然后对各尺度分解的信号分别选用不同的滤波参数,进行自适应滤波处理,并用该方法对液压系统运行中采集的压力信号进行降噪处理.试验结果表明,该方法比普通的自适应滤波方法能更有效地消除流体压力信号中的噪声.     

基于小波熵的低误报率人体热释电红外信号识别

提出一种基于双密度双树复小波变换小波熵特征的热释电红外(PIR)信号人体识别方法.首先对人体和狗的PIR探测器输出信号进行去噪预处理,然后提取信号的双密度双树复小波变换的小波熵作为特征,最后采用最小二乘支持向量机对特征进行分类.实验结果表明:所提取的特征及分类方法对人体与狗的热释电红外信号的识别率可达93.6%.因此该识别方法能大大降低PIR探测器的误报率,并可进一步提升PIR探测器在安防和智能家居系统中应用.     

非平稳背景噪声下声音信号增强技术

在电力电缆故障精确定位中声磁同步法由于具有精度高与抗干扰能力强的优点而得到广泛的应用,但放电声音信号的有效检测是其难点。由于小波包变换在检测正常信号中是否含有瞬态异常现象方面具有独特的优势,自适应滤波器具有对信号和噪声的先验知识需求少的特性以及遗传算法具有不依赖于具体问题的优点,提出了一种基于小波包变换分解信号、自适应滤波估计噪声与遗传算法寻优重构相结合的声音信号增强算法。实验研究表明,该算法精确性高、鲁棒性强,尤其适用于电缆故障点放电声不明显时声音信号提取的情况,从而解决了电缆故障精确定位中对背景噪声要求高、识别范围小的问题。     

Precise measurement of defect width in tapered roller bearing using vibration signal

The present experimental investigation is focused on establishing a robust signal processing technique to measure the width of the defect present on the outer or inner race of a tapered roller bearing. An experiment has been designed with roller bearings having various widths of seeded faults, on outer and inner races, respectively. The corresponding vibration signals have been investigated with the proposed method. This method initially denoises the vibration signal using un-decimated wavelet transform. The approximation signal has been shown to be effective for further time–frequency analysis using continuous wavelet transform (CWT). It is not only difficult but ambiguous as well to detect the entry and the exit points of the defect. The ambiguity gets reduced by using Symlet wavelet due to its linear phase nature which maintains sharpness in the signal even when there is a sudden change in signal. In the first phase of the measurement, the scalogram generated from CWT is used to measure the time duration that the roller takes to roll over the defect. However, measurement process is dramatically enhanced with the proposed ridge spectrum, which is generated from the CWT scalogram. The vertical strips drawn on the ridge spectrum corroborates well with defect width. Summarizing, the proposed method can be reckoned suitable and reliable in measuring bearing defect width in real-time from vibration signal.     

改进小波去噪Teager算子的齿轮微弱故障提取方法

针对齿轮箱在强噪声背景下齿轮微弱故障振动信号的特征不易被提取的问题,提出将改进小波去噪和Teager能量算子相结合的微弱故障特征提取方法。采用改进小波阈值函数对振动信号进行去噪处理,与形态学滤波和传统小波阈值函数相比能够有效地提高信号的信噪比。对去噪后的信号进行集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)得到若干本征模式函数(intrinsic mode function,简称IMF),计算各IMF分量与原信号的相关系数并结合各IMF分量的频谱剔除虚假分量。对有效的IMF分量计算其Teager能量算子,并重构得到Teager能量谱,对重构信号进行时频分析并将其结果与原信号的希尔伯特黄变换(HilbertHuang transform,简称HHT)得到的边际谱进行对比。实验研究结果表明,本研究方法相比HHT能够对齿轮微弱故障特征进行更为有效地提取,验证了本研究方法在齿轮箱微弱故障诊断中的可行性。     

PARAMETERS OPTIMIZATION OF CONTINUOUS WAVELET TRANSFORM AND ITS APPLICATION IN ACOUSTIC EMISSION SIGNAL ANALYSIS OF ROLLING BEARING

Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm,an opti-mization strategy for the waveform parameters of the mother wavelet is proposed with wavelet en-tropy as the optimization target. Based on the optimized waveform parameters,the wavelet scalogram is used to analyze the simulated acoustic emission (AE) signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT.     

基于LCD的自适应小波脊线解调及齿轮故障诊断

针对最佳小波参数的设定和齿轮裂纹故障振动信号频率成分复杂、信噪比低等问题,将遗传优化算法、小波脊线解调与局部特征尺度分解(local characteristic-scale decomposition,简称LCD)相结合,提出了基于LCD的自适应小波脊线解调方法。首先,采用LCD方法将原始信号分解为若干个内禀尺度分量(intrinsic scale component,简称ISC),并通过选择蕴含特征信息的ISC来实现信号降噪;然后,以小波能量熵为目标函数,采用遗传算法优化小波参数,得到自适应小波;最后,通过自适应小波分析提取ISC的小波脊线,从而实现对原始信号的解调分析。通过齿轮裂纹故障诊断实例验证了该方法的有效性和优越性。     

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two‐dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub‐band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub‐band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.     

DT-CWT相关滤波在齿轮箱故障诊断中的应用

根据小波系数的相关分析理论,提出了基于双树复小波变换的小波相关滤波法。该方法根据相邻层小波系数的相关性,通过迭代过程自适应地进行滤波,能够在达到良好降噪效果的同时保留微弱故障特征信息。对降噪后的信号进行希尔伯特包络分析便可准确得到故障特征频率。试验信号分析与工程应用结果表明,该方法能够有效提取强背景噪声下的齿轮箱轴承早期故障特征信息。     

采煤机摇臂齿轮传动系统振源定位分析方法

为了研究采煤机摇臂传动齿轮的振动分析方法并进行实机振源定位验证,首先,采用小波分析对采煤机摇臂振动信号进行降噪处理和频谱分析,依据特征频率下的振幅结果确定故障齿轮的啮合频率;然后,通过Morlet小波包络解调分析获取边频带信号频谱特征,依据边频带特征频率下的振幅结果确定故障齿轮的转动频率;最后,对频谱分析和Morlet小波包络解调分析的结果进行综合分析,锁定故障齿轮的准确位置。对一台国产采煤机摇臂齿轮传动系统进行了振动测试与信号分析,结果表明,基于小波分析、频谱分析和Morlet小波包络解调分析相结合的振动分析方法可以实现对采煤机摇臂故障齿轮的准确定位,为强噪声环境下复杂齿轮传动系统的故障快速定位和现场定点维修提供了方法支持。     

基于自适应最优Morlet小波的滚动轴承故障诊断

滚动轴承早期故障信号中故障信息比较微弱常常被强噪声所掩盖,增加了对滚动轴承故障诊断的难度。针对这一问题,笔者提出了基于自适应最优Morlet小波变换的滚动轴承故障诊断方法。首先,利用粒子群优化算法对Morlet小波变换的核心参数进行自适应寻优,在获得最优Morlet小波的同时保证了良好的带通滤波性能;然后,将最优Morlet小波对滚动轴承早期故障信号进行滤波去噪,提高信号的信噪比;最后,对最优Morlet小波滤波信号进行包络谱分析,通过包络谱中的主导频率成分与滚动轴承各元件的故障特征频率对比从而判断轴承的故障位置。仿真数据和实测数据分析结果证明,笔者所提方法能够有效提取故障信号中的特征信息,具有一定的有效性。     

基于同步压缩小波变换的滚动轴承故障诊断

针对滚动轴承故障诊断中存在的非平稳故障信号的特征提取困难这一难题,提出利用同步压缩小波变换(SWT)对故障信号的监测数据进行处理的方法。首先对信号进行连续小波变换(CWT),其次对小波变换系数进行同步压缩变换(SST),然后对SST系数进行自适应阈值去噪,之后在有效信号数据的频率中心附近进行积分提取,最后用提取到的有效信号进行重构。对实测的滚动轴承故障信号进行处理验证,结果表明,SWT具有较高的信号提取精度以及降噪能力,同时具有较高的时频分辨率,能够将故障信号转换为高分辨率的时频谱,弥补了CWT在这方面的不足。     

新的基于小波变换的振动信号消噪方法

噪声消除是小波变换最成功的应用之一,其基本思想是将信号的小波变换系数与给定的门限比较,保留比门限大的系数,而将其他的置零,然后进行小波重构。这种小波变换消噪方法很可能将信号中一些有用的小能量分量当成噪声消除。根据旋转机械振动信号的循环平稳性特征,提出了一种新的基于小波变换的振动信号消噪方法,并用数字试验信号和碰摩试验振动信号对新消噪方法和Matlab提供的小波消噪方法的性能进行了比较测试。结果表明,在振动信号消噪方面,新方法相比传统的小波消噪方法有更好的性能,能够有效地抑制信号中处于各频段的噪声分量。     

Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct

This paper presents a new adaptive algorithm for active noise control (ANC) that can be effectively applicable to a short acoustic duct, such as the intake system of an automobile engine, where the stability and fast convergence of the ANC system is particularly important. The new algorithm, called the modified-filtered-u LMS algorithm (MFU-LMS), is developed based on the recursive filtered-u LMS algorithm (FU-LMS) incorporating the simple hyper-stable adaptive recursive filter (SHARF) to ensure the control stability and the variable step size to enhance the convergence rate. The MFU-LMS algorithm is implemented by purely experimental ways, and is applied to active control of noise in a short acoustic duct, and is validated using two experimental cases of which the primary noise sources are a sinusoidal signal embedded in white noise and a chirp signal. The experimental results demonstrate that the proposed MFU-LMS algorithm gives a considerably better performance than other conventional algorithms, such as the filtered-x LMS (FX-LMS) and the FU-LMS algorithms.     

基于dbN小波变换和自适应高斯滤波的齿面干涉图像相位滤波方法

齿轮齿面形貌的激光干涉测量中，由于齿面高度差较大，采集到的干涉图像中难以避免存在条纹密集区域，容易出现局部条纹粘连、错切等现象，增加了相位噪声和解包裹难度。分析了包裹相位图中条纹密度分布规律，提出了一种基于dbN小波变换和自适应高斯滤波的齿面干涉图像相位去噪方法。首先，利用小波变换分解出包裹相位图中常表现为高频信号的噪声，采用软阈值去噪滤除部分高频噪声；其次，根据包裹相位图频域特征，结合自适应高斯滤波进一步对高频噪声进行迭代滤波处理；最后，设计了相关实验，通过与经典的滤波方法进行对比，所提方法不仅能够有效滤除条纹较为密集的包裹相位图中的相位噪声，而且更大限度地保留了图像细节信息，证明了所提方法的有效性和正确性。     

基于TMS320C6713的去噪自适应滤波器硬件实现

我们每天都会受到音频噪音的影响,原始信息会受到周围环境噪声信号的污染。为此,本文研究了一个多抽头自适应去噪实时硬件系统,它利用TMS320C6713上实现的最小均方算法（LMs）来去除与音频相关的应用程序中接收到的不期望出现的噪声信号。文章首先介绍了最小均方算法的c语言实现并在CodeComposerStudio上进行仿真,最后在C6713上实现。考虑不同的音频输入,进行了三项实验来测试所设计的自适应去噪系统的效率。实验采用300、500、800、1000和3000Hz的音频信号及男性语音信号为输入的参考信号,持续检测信号中的噪声,直至将它全部去除。此外,还研究了与自适应去噪系统性能相关的收敛速度、滤波器安排顺序以及信噪比。实验结果表明,所设计系统其信噪比有很大的改善。