Retrieving the time history of displacement from measured acceleration signal

It is intended to retrieve the time history of displacement from measured acceleration signal. In this study, the word retrieving means reconstructing the time history of original displacement signal from already measured acceleration signal not just extracting various information using relevant signal processing techniques. Unlike extracting required information from the signal, there are not many options to apply to retrieve the time history of displacement signal, once the acceleration signal is measured and recorded with given sampling rate. There are two methods, in general, to convert measured acceleration signal into displacement signal. One is directly integrating the acceleration signal in time domain. The other is dividing the Fourier transformed acceleration signal by the scale factor of -ω² and taking the inverse Fourier transform of it. It turned out both the methods produced a significant amount of errors depending on the sampling resolution in time and frequency domain when digitizing the acceleration signals. A simple and effective way to convert the time history of acceleration signal into the time history of displacement signal without significant errors is studied here with the analysis on the errors involved in the conversion process.     

基于小波分析的离心式注水泵机械振动实时监测方法

为保证注水泵机组安全稳定运行,减少管理人员工作量,提出基于小波分析的离心式注水泵机械振动实时监测方法。探究注水泵作业流程与结构特征,结合多种常见故障类型分析结果,构建水泵输入、输出功率与生产效率的动力学模型;考虑到注水泵特性,以压电加速度传感器作为主要监测设备,明确信号采样要求,通过量化处理将模拟信号变换为数字信号,便于信号分析;当监测信号内低频成分丰富时,确定母小波和变换系数,经伸缩与平移处理完成连续小波变换与反变换;当低频分量不足时,引入小波包理论,分割小波空间,合理分解不同频带的信号,保证监测信息不丢失,获取信号特征,实现机械振动实时监测。仿真实验证明,该方法具有较强的信号处理能力,可通过监测信息准确判断出设备故障类型。     

基于MPE-CEEMD算法的轴流压缩机旋转失速状态识别

以某型风洞压缩机出口机壳处的声压信号为研究对象,建立以多尺度排列熵均值(MMPE)为筛选CEEMD分解后的集成平均分量的体系,提出了一种MPE-CEEMD算法。将尺度[1,5]范围内声压仿真信号的多尺度排列熵的最大值作为筛选集成平均分量的阈值,剔除异常分量后对重构后的信号做傅里叶变换。若在频谱图上存在小于轴频,且随着流量的减小,而幅值增大的特征频率,那么该特征频率可以判定为旋转失速特征频率。通过分析风洞压缩机在1.3Ma,叶片角度20°,压比1.107 1工况下的数据,发现该算法可以有效的剔除声压信号中的异常成分。     

基于S变换的热连轧机耦合振动特征提取

提出一种基于短时傅里叶变换的自适应频域滤波方法,将噪声信号与振动特征成功地分离。根据短时傅里叶变换和功率法设定的阀值,自动捕捉了振动信号在不同时间段的优势频率。对振动信号、压下液压缸压力信号和伺服阀给定信号做短时傅里叶变换后,热连轧机振动被诊断为液机耦合振动。利用离散小波变换和S变换相结合的方法对轧机振动信号进行分析,确定轧机起振的时间为液压压下系统的投入时间,证明了热连轧机存在液机耦合振动现象。     

基于改进经验小波变换的行星齿轮箱故障诊断

行星齿轮箱振动信号具有复杂多分量和调幅-调频的特点。幅值解调和频率解调方法能够避免传统Fourier频谱中的复杂边带分析,有效识别故障特征频率。经验小波变换通过对信号Fourier频谱的分割构造一组正交滤波器组,能提取具有紧支撑Fourier频谱的单分量成分,再对单分量成分运用Hilbert变换即可实现信号的解调分析。经验小波变换能够有效分离出调幅-调频成分,不存在模态混叠现象,具有完备的理论基础,自适应性好、算法简单、计算速度快。将改进的经验小波变换应用于行星齿轮箱振动信号的解调分析;提出了一种单分量个数的估算方法,解决了经验小波变换中的Fourier频谱划分问题;给出了对故障敏感的信号分量的选取方法,提高了分析的针对性。将改进方法应用于行星齿轮箱振动仿真信号和实验信号分析,验证了该方法的有效性。     

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.     

基于采样逼近的准同步改进算法研究

准同步算法在和离散傅里叶变换结合求取周期信号的幅值和相位时,由于被测信号频率未知或仅知道被测信号的频率变化范围,在求解傅里叶系数时只能将采样频率作为被测信号频率带入造成傅里叶系数计算产生理论近似,尤其是在求解各次谐波初相角时频偏越大,求解相位误差相应增大。本文首先基于准同步测频差原理求出被测信号的实际频率,重新选取采样点使其逼近整周期采样点数,再将计算频率代入傅立叶变换中的基函数频率,最后迭代求取傅里叶系数。计算结果表明这种基于频率代入并采样逼近的改进准同步算法可以极大地提高准同步谐波算法的准确度。     

一种线性调频连续波探测抗雷达辐射源干扰方法

随着各类军用、民用辐射源频段的不断扩展以及功率的不断提升,线性调频连续波(LFMCW)探测器面对此类干扰的威 胁也愈加严重。 然而,目前关于 LFMCW 体制抗干扰技术的研究主要围绕在对抗有源干扰,鲜有针对强辐射源干扰的研究。 针 对上述问题,以对抗大功率雷达辐射源干扰为背景,本文对常规脉冲和线性调频脉冲信号两种典型的雷达辐射源信号进行建 模,并分析其对 LFMCW 探测器的干扰机理。 根据目标回波信号和干扰信号在时频域上的差异,提出了一种基于时频变换和边 缘检测的干扰抑制算法。 首先使用短时傅里叶变换(STFT)获得接收信号的时频图像,利用脉冲干扰在时间轴上的周期截断特 征,在时频域进行干扰的粗滤波;然后结合边缘检测技术,沿着频率轴使用 Sobel 算子进行卷积,进一步滤除残留的干扰,重构 滤波后的频谱,提取目标信息。 仿真及实验结果表明,该算法能够有效地抑制两种典型的脉冲体制雷达辐射源信号干扰,在干 扰背景下提取目标差频频率的精度优于 3% 。     

轨道交通扣件系统时频特性测试与分析

为了得到安装在不同扣件系统下钢轨的振动特性,室内以WJ 7型扣件为样品,采用力锤激振法对其进行动力测试。基于MATLAB软件信号分析处理平台,利用线性短时傅里叶变换,非线性Page变换和Zhao Atlas Marks变换对振动信号的时频特性进行对比分析。研究结果表明：钢轨轨脚振动主要集中在中高频,其中在2 250 Hz振动能量最大,且持续时间最长;短时傅里叶变换(Short-time Fourier transform,简称STFT)具有较高的时间和频率分辨率,可以将轨脚的时域、频域和时频域的固有属性一一对应起来;非线性Page时频分析在能量较高的高频段具有较好的分辨率,而在低频段显得无能为力;非线性Zhao-Atlas-Marks分布时频分析在低频段效果较好,在高频段不是很理想。该分析结果可为轨道结构的振动噪声控制提供依据。     

A novel approach for realization of primary vibration calibration standard by homodyne laser interferometer in frequency range of 0.1 Hz to 20 kHz

The paper discusses the primary vibration calibration standard of NPL, India capable of calibrating the reference accelerometers in frequency range from 0.1 Hz to 20 kHz as per ISO 16063-11. The excitation subsystem produces constant vibration at a specified amplitude and frequency, while the measurement system uses NI interface for measuring the quadrature output. The acceleration level and voltage level at the calibration frequency f is determined by applying a Discrete Fourier Transform to the voltage and displacement signals, and then examining the spectral component at frequency f. A PC-based data acquisition system acquires the accelerometer voltage signal and analog quadrature interferometer photodetector signal pair as well as a digital quadrature pair whereby the software processes the demodulated photodetector signals to reconstruct the armature displacement. The validation of the calibration results for standard reference accelerometers with manufacturer results and uncertainty in calibration in entire frequency range 0.1 Hz to 20 kHz is reported in the present work.     

HHT-based nonlinear signal processing method for parametric and non-parametric identification of dynamical systems

This paper presents a time–frequency signal processing method based on Hilbert–Huang transform (HHT) and a sliding-window fitting (SWF) technique for parametric and non-parametric identification of nonlinear dynamical systems. The SWF method is developed to reveal the limitations of conventional signal processing methods and to perform further decomposition of signals. Similar to the short-time Fourier transform and wavelet transform, the SWF uses windowed regular harmonics and function orthogonality to extract time-localized regular and/or distorted harmonics. On the other hand, HHT uses the apparent time scales revealed by the signal's local maxima and minima to sequentially sift components of different time scales, starting from high- to low-frequency ones. Because HHT does not use pre-determined basis functions and function orthogonality for component extraction, it provides more accurate time-varying amplitudes and frequencies of extracted components for accurate estimation of system characteristics and nonlinearities. Methods are developed to reduce the end effect caused by Gibbs’ phenomenon and other mathematical and numerical problems of HHT analysis. For parametric identification of a nonlinear one-degree-of-freedom system, the method processes one free damped transient response and one steady-state response and uses amplitude-dependent dynamic characteristics derived from perturbation analysis to determine the type and order of nonlinearity and system parameters. For non-parametric identification, the method uses the maximum displacement states to determine the displacement–stiffness curve and the maximum velocity states to determine the velocity-damping curve. Moreover, the SWF method and a synchronous detection method are used for further decomposition of components extracted by HHT to improve the accuracy of parametric and non-parametric estimations. Numerical simulations of several nonlinear systems show that the proposed method can provide accurate parametric and non-parametric identifications of different nonlinear dynamical systems.     

基于双时域微弱故障特征增强的轴承早期故障智能识别*

针对轴承早期微弱故障难以准确识别的问题,提出一种基于双时域微弱故障特征增强的轴承早期故障智能识别方法。利用广义S变换和Fourier逆变换推导出一种双时域变换,将轴承振动信号变换为双时域二维时间序列。根据双时域变换的能量分布特点,提取二维时间序列的主对角元素以构建故障特征增强的时域振动信号。仿真信号和轴承故障信号分析验证了双时域微弱故障特征增强的可行性和有效性。采用脉冲耦合神经网络和支持向量机对增强后的轴承信号进行时频特征参数提取和智能识别,平均识别精度达到了95.4%。试验结果表明所提方法能有效提高轴承早期故障的智能识别精度。     

基于频率-波数的频域合成孔径导波成像研究

为实现管道中缺陷位置与尺寸的准确预测,在爆炸反射成像原理的基础上,提出了一种基于频率-波数的频域合成孔径导波成像算法。通过磁致伸缩方式在管道中激励出T(0,1)模态导波,对采集到的回波信号做二维傅里叶变换并进行角谱运算对频域内声场进行重构。最后,对其进行反傅里叶变换后实现目标区域内的聚焦成像。通过实验验证成像结果,同时与原始B扫结果进行了对比。结果表明,所提算法有效抑制了旁瓣效应产生,使成像分辨率提高了约30%,定量误差缩减了26.1%;同时研究表明在缺陷轴向位置、深度及倾斜角度发生改变时,利用该算法实现缺陷图像重构后,其检测准确度只受缺陷周向范围的绝对尺寸影响,对周向表面缺陷检测具体较高的灵敏度。     

Extended Kalman Filter frequency tracker for nonstationary harmonic signals

In this paper, we propose a frequency tracking algorithm based on an Extended Kalman Filter (EKF). We introduce a generalized state space model to estimate and track frequency of a harmonic signal embedded in broad-band noise. Such nonstationary noisy harmonic signals are characterized by time-varying frequencies and amplitudes. Developing a modified state-space model, we improve performance of EKF frequency tracker for these signals. The proposed method is also used in an adaptive algorithm to estimate noise variance which is assumed to be unknown. Simulation results reveal superiority of the proposed method compared with typical EKF, short time Fourier transform, and interpolated discrete Fourier transform.     

Periodic error quantification for non-constant velocity motion

This paper describes a procedure for analyzing non-stationary (variable frequency content) periodic error signals obtained during accelerating or decelerating motion. This capability is important due to the recent interest in real-time compensation of periodic error for precision positioning systems. In order to apply the spatial Fourier transform to the non-stationary signal, the constant time interval signals are resampled by linear interpolation using a constant spatial interval. Numerical and experimental results are provided for constant acceleration and sinusoidal motion profiles.     

基于改进型峰值搜索法的变速箱振动阶比分析

基于传统内燃机汽车发动机引起振动噪声阶次特征明显的特点，运用短时傅里叶变换（short-time Fourier transform，简称STFT）进行转速估计，结合阶次追踪法，对汽车加速工况变速器振动信号进行阶次分析。首先，利用STFT对加速工况变速箱振动信号进行时频分析；其次，利用改进型峰值搜索法提取特征阶次所对应的瞬时频率值，进一步计算得到发动机转速信号表达式；然后，根据发动机转速信号表达式对振动信号在角域重采样，进行阶次分析；最后，利用本研究方法对变速箱加速过程振动信号进行阶次分析，并与商用软件LMS.Test.lab分析结果进行对比。结果表明，本研究方法无需布置转速传感器即可对变速箱振动信号进行阶次分析，为整车振动噪声试验分析提供参考。     

A vibration analysis method based on hybrid techniques and its application to rotating machinery

Vibration-based condition monitoring and fault diagnosis technique is a most effective approach to maintain the safe and reliable operation of rotating machinery. Unfortunately, the vibration signal always exhibits non-linear and non-stationary characteristics, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on hybrid techniques is proposed in this paper. Firstly, the raw signals are decomposed into a few product functions (PFs) using local mean decomposition (LMD), and meanwhile instantaneous frequency and instantaneous amplitude also are obtained. Subsequently, Fourier transform is performed on the derived PFs, and then, according to the spectra features, the useful PFs are selected to reconstruct the purified vibration signals. Lastly, several different fault features are fused to illustrate the operating state of the machinery. The experimental results show that the proposed method can accurately extract machine fault features, which proves that the combined application of LMD and other signal processing techniques is a successful scheme for the machine vibration analysis.     

基于IEWT和MCKD的滚动轴承故障诊断方法

针对经验小波变换（Empirical wavelet transform,EWT）对强噪声环境中滚动轴承微弱故障诊断的不足,主要是傅里叶频谱分段不当的问题。提出一种基于最大相关峭度解卷积（Maximum correlated kurtosis deconvolution,MCKD）降噪与改进EWT相结合的滚动轴承早期故障识别方法。首先采用最大相关峭度解卷积算法以包络谱的相关峭度最大化为目标对原信号进行降噪处理、检测信号中的周期性冲击成分,然后根据信号Fourier频谱的包络极大值进行分段,通过分析各频段平方包络谱中明显的频率成分来诊断故障。新方法能有效降噪、增强信号中周期性冲击特征、降低单次偶然冲击的影响、抑制非冲击成分。通过对含外圈、内圈故障的滚动轴承进行试验分析,结果表明,相比于快速谱峭度图和小波包络分析方法,该方法提取出的特征更加明显,能有效实现滚动轴承早期微弱故障的识别。     

自适应改进双树复小波变换的齿轮箱故障诊断

针对双树复小波变换存在频率混叠以及参数需自定义的缺陷,提出自适应改进双树复小波变换的齿轮箱故障诊断方法。首先,利用双树复小波变换将信号进行分解和单支重构,采用粒子群算法将分解后分量峭度值作为适应度函数,选择双树复小波的最优分解层数;其次,对重构出的低频信号进行频谱分析提取故障特征,将单支重构后的各高频分量进行变分模态分解,通过峭度值获得各高频分量经变分模态分解后的主频率分量信号;最后,分析各主频率分量信号的频谱,识别齿轮箱的故障特征。结果表明,该方法与双树复小波变换和变分模态分解相比,不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且还提高了从强噪声环境中提取瞬态冲击特征的能力。