连续小波变换在检测强载波干扰中局放信号的应用

讨论八种常用连续小波变换检测强载波干扰中局放信号的效果,并对如何正确选择小波进行了分析。仿真结果表明,采用合适的连续小波及伸缩因子既可对局放信号进行特征提取,又可对强载波干扰信号进行抑制,因此应用合适的连续小波变换检测强载波干扰中的局放信号是可行的。     

连续小波变换应用于电力系统行波奇异性检测的探讨

主要讨论了解决零模行波波头定位的问题，由于零模衰减严重，波头比较光滑，因而使用离散小波变换无法定位零模波头。文中将连续小波变换应用于定位零模波头中，在理论分析的基础上，得出了利用连续小波变换定位零模波头时小波基和小波变换尺度的选择原则，利用该原则的小波变换可以有效地定位零模波头，EMTP仿真证明了作用基于该原则的小波变换可以准确定位零模波头。     

小波基函数在故障诊断中的最佳选择

通过定性与定量的分析,提出了在对冲击信号进行连续小波变换时选择最佳小波基函数的方法和小波变换 后故障特征提取效果优劣的检验手段,并且得出了对于冲击性信号的连续小波变换,小波基函数的最佳选择为 Morlet函数的结论。     

基于多线程技术的连续小波变换实现

简要介绍了连续小波变换(CWT)的快速算法,讨论了采用多线程技术编程实现CWT的原理及方法。实验表明,该方法可改善CWT在工程应用中的性能。     

用开关电流技术实现连续小波变换的改进方法

提出了一种用开关电流技术实现连续小波变换的改进方法，即采用梯形公式逼近数值积分的原理改进用于逼近连续小波变换的带通滤波器组，并用开关电流电路实现，还详细分析了这种改进对于减小逼近误差的优势所在．以开关电流的双二次滤波器实现八通道的Marr小波为例，简述了设计过程和仿真结果，从而证实了这种改进的可行性．     

Improved wavelet analysis for induction motors mixed-fault diagnosis

Eccentricity is one of the frequent faults of induction motors, and it may cause rub between the rotor and the stator. Early detection of significant rub from pure eccentricity can prolong the lifespan of induction motors. This paper is devoted to such mixed-fault diagnosis: eccentricity plus rub fault. The continuous wavelet transform (CWT) is employed to analyze vibration signals obtained from the motor body. An improved continuous wavelet transform was proposed to alleviate the frequency aliasing. Experimental results show that the proposed method can effectively distinguish two types of faults, single-fault of eccentricity and mixed-fault of eccentricity plus rub. Translated from Proceedings of the Chinese Society for Electrical Engineering, 2006, 26(8): 159–162 [译自: 中国电机工程学报]     

汽车前端冷却模块流场模拟与试验研究

本文利用CFD分析与风洞测试两种手段对整车前端冷却模块气流场进行分析验证,通过对4个车速即(20、50、90和110)km/h下的15个测点的气流情况进行分析得出,CFD分析与试验结果十分吻合,计算值与试验值的误差在可接受的范围内。本工作验证了CFD分析的准确性,对于整车前端冷却模块流场的考察完全可以用CFD模拟代替试验,节省开发费用,对整车开发有重要的指导意义。     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

电力系统强迫振荡源的时频域定位方法

快速、准确定位电力系统强迫振荡源,对于预防由强迫振荡引起的电力系统大停电事故意义重大,但目前的强迫振荡源时域定位方法计算过程较为复杂,计算效率有待提升.为此,文中提出一种基于广域量测信息的电力系统强迫振荡源的时频域定位方法,该方法首先将电力系统广域量测数据进行连续小波变换,计算各小波系数矩阵的小波相对能量,进而甄别出电...     

基于复阻抗咽造影的吞咽事件检测与智能识别方法

吞咽障碍早期筛查是降低吞咽障碍发病率的重要手段,而对吞咽事件(SE)的准确识别是吞咽障碍筛查和治疗过程中的关键环节。阻抗咽造影(IPG)是一种新型非侵入式吞咽事件检测方法,但现有的IPG技术仅检测阻抗幅值而忽略了同样重要的相位信息。为了实现对吞咽事件的全面检测及智能识别,该文提出一种基于整周期数字锁相放大原理的复阻抗咽造影(CIPG)检测方法,设计了基于FPGA的CIPG检测系统以连续描记吞咽过程的复阻抗(阻抗幅值和相位)信息,并设计了基于连续小波变换(CWT)和GoogLeNet相结合的吞咽事件智能识别算法。设计了包含喝水、干咽、吃面包、吃酸奶、咳嗽等5种吞咽事件的识别实验,实验结果表明,仅利用阻抗幅值信息时的吞咽事件识别准确率为86.1%,而同时利用阻抗幅值和相位信息时的识别准确率为95.7%,后者的识别准确率高于其它算法。该研究证实了CIPG技术和吞咽事件智能识别算法的有效性与优越性,为下一步开发基于CIPG的吞咽障碍早期筛查方法奠定了理论和技术基础。