NEW METHOD FOR WEAK FAULT FEATURE EXTRACTION BASED ON SECOND GENERATION WAVELET TRANSFORM AND ITS APPLICATION

A new time-domain analysis method that uses second generation wavelet transform (SGWT) for weak fault feature extraction is proposed. To extract incipient fault feature, a biorthogonal wavelet with the characteristics of impact is constructed by using SGWT. Processing detail signal of SGWT with a sliding window devised on the basis of rotating operation cycle, and extracting modulus maximum from each window, fault features in time-domain are highlighted. To make further analysis on the reason of the fault, wavelet package transform based on SGWT is used to process vibration data again. Calculating the energy of each frequency-band, the energy distribution features of the signal are attained. Then taking account of the fault features and the energy distribution, the reason of the fault is worked out. An early impact-rub fault caused by axis misalignment and rotor imbalance is successfully detected by using this method in an oil refinery.     

基于独立成分分析(ICA)处理两相流信号

为了提高固体速度测量精度，提供一种基于独立成分分析（ICA）处理两相流信号的基本方法。首先介绍独立成分分析（ICA）的基本原理，然后介绍使用空间滤波和小波变换获得固体速度的方法。最后给出仿真实验结果证明该方法可行。     

最优Morlet小波滤波及其在机械故障特征分析中的应用

分析了Morlet小波及其滤波特性,结合小波变换技术和奇异值分解技术,提出了基于最优Morlet小波和奇异值分解的滤波消噪方法,解决了传统小波去噪方法的不足,即如何选取小波基、阈值方法和阈值。依据最优Morlet小波滤波方法,研发了基于小波变换的故障特征分析仪,并应用于轴承故障的特征提取,实验结果表明,该方法具有良好的去噪性能,用于故障特征提取是有效的。     

数学形态滤波器及其在心电图机中的应用

在心电图信号的测量中,存在很强的噪声和干扰,这些干扰噪声往往与信号的频率相 有效地保存有用信号的情况下最大限度地抑制心电信号测量中噪声,历来就是技术上的一个难点。本文探讨了应用非线性的数学形态滤波到心电图机中并取得良好的效果。     

Contourlet域手写签名鉴别特征提取

手写签名验证是一种根据手写笔迹判断书写人身份的一门科学和技术。与联机签名鉴定相比,脱机签名鉴别受设备约束少,具有更广的实用范围。然而,由于脱机签名鉴定丢失了书写过程中的动态信息,鉴定难度大。本文针对脱机手写签名鉴定的特点,提出了基于Contourlet和分形维的特征选取方法,将传统的结构特征与统计特征有机结合起来。运用K-L变换对已提取的特征向量进行降维,最后输入支持向量机进行真伪鉴别。实验结果表明了本文算法的高识别性。     

An adaptive Morlet wavelet filter for time-of-flight estimation in ultrasonic damage assessment

Ultrasonic non-destructive testing has been widely used in assessing the integrity of engineering materials such as high-temperature alloys and structures such as pipelines, bridges, and other load-bearing structures. The ultrasonic signals received from these structures are often noisy. Effective noise-reduction techniques are needed in order to accurately assess their condition. This paper presents a new digital signal processing method for estimating ultrasonic time-of-flight diffraction (TOFD). This method is based on wavelet analysis using the Morlet wavelet and the least mean squares (LMS) adaptive filter. The adaptive line enhancer (ALE) structure is used for the adaptive filter. The filter is designed to remove noise and identify the point at which the ultrasonic signal starts to reflect an echo from the tip of a crack. Both simulated and experimental data obtained from a steel plate with a crack produced by electrical-discharge-machining (EDM) are used to demonstrate the performance of the proposed method. This method is especially useful when the properties of the crack signal are unknown and the signal-to-noise ratio is low.     

Study of small size wavelet transform processor and wavelet inverse-transform processor using SAW devices

The objective of this research was to investigate the possibility of reducing the sizes of dyadic wavelet transform processor and dyadic wavelet inverse-transform processor using surface acoustic wave (SAW) devices. The motivation for this work was prompted by these processors which are of large sizes. Although these processors are being used, many fields need small size processors, so this work proposes two novel methods of reducing the sizes of these processors: firstly, the architecture which is that various interdigital transducers (IDTs) stand in a line is used to reduce the sizes of these processors. In this architecture, when the electrode-pairs numbers of various IDTs are larger than 20, the bulk acoustic wave (BAW) is eliminated, so this architecture cannot only reduce the sizes of these processors, but also eliminate BAW; secondly, as long as the electrode-overlap envelopes of IDTs will weaken fast with time t, the sizes of these processors are also reduced.     

Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump

The fault diagnosis problem is conceived as a classification problem. In the present study, vibration signals are used for fault diagnosis of centrifugal pumps using wavelet analysis. Rough set theory is applied to generate the rules from the vibration signals. Based on the strength of the rules the faults are identified. The different faults considered for this study are: pump at good condition, cavitation, pump with faulty impeller, pump with faulty bearing and pump with both faulty bearing and impeller. However, the classification accuracy is based on the strength and number of rules generated using rough set theory. Wavelet features are computed using Discrete Wavelet Transform (DWT) from the vibration signals and rules are generated using rough sets and classified using fuzzy logic. The results are presented in the form of confusion matrix which shows the classification capability of wavelet features with rough set and fuzzy logic for fault diagnosis of monoblock centrifugal pump.     

Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions

Fast Fourier transform (FFT) analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always provide good results for the cases of load torque, speed and voltages variation, leading to a variation of the motor-slip and the consequent FFT problems that appear due to the non-stationary nature of the involved signals. In this paper, the discrete wavelet transform (DWT) of the apparent-power signal for the airgap-eccentricity fault detection in three-phase induction motors is presented in order to overcome the above FFT problems. The proposed method is based on the decomposition of the apparent-power signal from which wavelet approximation and detail coefficients are extracted. The energy evaluation of a known bandwidth permits to define a fault severity factor (FSF). Simulation as well as experimental results are provided to illustrate the effectiveness and accuracy of the proposed method presented even for the case of load torque variations.     

采用余弦调制滤波器组泄漏检信号处理研究

供水管道泄漏检测定位中,定位的不确定度由检测信号的信噪比决定,抑制噪声干扰,提高信噪比,是提高泄漏检测定位精度的关键。在充分分析泄漏信号特征的基础上,提出将改进的余弦调制滤波器组用于泄漏检测信号的噪声抑制。该方法利用分析滤波器组将信号分解为若干个带宽相同的子带信号,然后选择有效子带,再通过综合滤波器组将有效子带信号合成。该方法是一种不需要泄漏信号和噪声先验知识的自适应信号处理方法,能够有效抑制噪声,由于余弦调制滤波器分解的特性,即使泄漏信号处于不同频率段时,噪声抑制效果基本相同,处理后信噪比较处理前平均提高8 d B左右。实际工程应用验证表明,该方法去噪处理后的定位结果优于现有各种噪声抑制/信号增强方法的结果。     

3-D Surface roughness profile of 316-stainless steel using vertical scanning interferometry with a superluminescent diode

Surface roughness is one of many parameters that influences on mass stability of standard weight, commonly used as a transfer standard of mass SI unit. One of the most famous non-invasive methods for determining surface roughness from a surface profile of material is a vertical scanning interferometry (VSI) with a white light source. In this research, 3-D surface profiles of 316-stainless steel, usually used as a material for standard weights, are constructed by using VSI, based on Michelson interferometer (MI). Because of its low-coherent properties, low cost, and compact light source, a superluminescent diode (SLD) is chosen as a low-coherence light source in our interferometry system. Since a continuous wavelet transform (CWT) algorithm provides accuracy results, it is also used as a numerical analyzing method for the interferogram signals, taking from our VSI. The surface roughness and measurement uncertainty, calculated from the constructed 3-D surface roughness profiles of 316-stainless steel samples, are discussed.     

基于FPGA的9/7小波变换算法实现

提出了一种基于现场可编程门阵列(FPGA)的高速9/7二维离散小波变换(2-D DWT)设计。在实现一维离散小波变换(1-D DWT)时采用多级流水线技术,并使用了改进的提升算法。同时,使用正则符号编码(CSD编码)和优化的移位加法操作实现乘法器,使其便于通过硬件实现,且加快了处理速度。在进行二维离散小波变换时采用改进的基于行的结构,只需完成3行行变换即可开始列变换,减少了系统资源的占用。设计通过MATLAB与ModelSim联合仿真,可以稳定运行在60MHz时钟频率下,完全能够满足高速图像实时处理的要求。     

小波神经网络在CCD图像噪声处理中的应用

目的：CCD相机响应功能的非线性,导致了CCD噪声模型的复杂性,使得滤波效果不佳,本文提出一种针对于数字图像中CCD噪声的小波神经网络滤波器。方法：首先,分析CCD噪声模型,找出导致CCD噪声模型复杂的原因——CCD相机响应功能（cameraresponsefunction简称CRF）的非线性;接着,在对ANS滤波器分析的基础上,针对影响滤波效果的两大问题：滤波窗口和图像强度,将小波神经网络非线性逼近CCD噪声曲线,按照噪声参数对图像进行区域划分并分配相应的权值,然后结合相应的非线性滤波器进行针对性滤波,最后综合输出。结果：实验结果表明：本文改进的滤波器滤波效果明显,信噪比得到进一步提高（24.65）。结论：利用神经网络良好的非线性函数逼近性,将其结合ANS滤波器构造出神经网络非线性ANS滤波器（NN-NANSfilter）,试验结果表明,该滤波器在去除噪声的同时边缘细节也得到了很好的保留,同时提高了信噪比。     

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.     

利用小波变换提高数字全息信噪比

光学和数字全息需要解决的基本问题是在图像重构过程中出现的散斑噪声,因为散斑降低了信噪比（SNR）。目前很多方法都可以减小散斑噪声,以提高数字全息信噪比。现利用小波滤波的方法来提高数字全息重构过程中的信噪比。提供了实验结果,实验结果证明该方法是可行的。     

红外双波段图像实时融合系统

选用高性能定点数字信号处理器DM642作融合系统核心处理器,S3C2410作辅助控制处理器,利用DM642的高速图像处理性能及它的视频编/解码单元无缝连接的灵活可配置视频端口,结合S3C2410强大的控制能力,研制了探测中波红外和长波红外的双波段图像实时融合系统。将一种基于离散小波变换的图像融合算法应用于红外双波段图像的融合,针对实时嵌入式系统的特点,对算法进行了优化,并将其移植在以DSP+ARM架构的嵌入式平台上。实验结果表明,该融合算法经仿真优化后,只需39.6ms即可完成两幅大小为320pixel×240pixel的红外图像的实时融合,满足25frame/s的工程需求,且融合后的图像能突出中波和长波红外各自的特点。