水下回波处理中分数阶傅里叶变换的带通采样实现方法

利用球形压电陶瓷自身所具有的耐压能力,采用径向极化空气背衬压电球壳换能器作为声学接收敏感元件,设计并制作了一种球形耐压水听器。首先对其低频开路接收灵敏度和谐振频率等声学特性进行了分析和有限元仿真,然后对其强度和稳定性等耐压性能进行了分析和有限元仿真,最后对其声学性能和耐压能力进行了测试。测试表明,该球形耐压水听器的直径为36 mm,工作频段为50 Hz10 kHz,低频接收灵敏度为􀀀198:4 dB(0 dB=1 V/Pa),等效自噪声谱级为46.5 dB@1 kHz,其耐压深度可达3000 m。该耐压水听器为大深度水听器设计提供了参考,在深水声学领域具有重要的应用价值。     

Multi component LFM signal detection and parameter estimation based on EEMD–FRFT

This paper studied multi component LFM signal detection and parameter estimation under the noise circumstance of various signal-to-noise ratios. Based on the analysis of fractional Fourier transform detection and parameter estimation on simple component LFM signal, this paper proposed the method of multi component LFM signal detection and parameter estimation based on EEMD–FRFT (Ensemble Empirical Mode Decomposition–Fractional Fourier transform), and this method was that with the EEMD algorithm, from the frequency domain decompose the analyzable signal to narrow-bandwidth components, whose center frequency changed from high to low, then accurately estimate the parameter and detect the signal of each component out of the pseudo-component with FrFT. This method solved the problem of mode aliasing of signal decomposition; meanwhile, the problem of detecting the multi component LFM signal would be simplified as the problem of one-dimensional search in small scope, which could reduce the amount of operation and improved the detection accuracy. A simulation computation for multi component LFM signal of various SNR (signal-to-noise ratios) was made and the result showed that the error of parameter estimation was less than 5% in the case of SNR not less than −10 dB.     

A time domain approach to diagnose gearbox fault based on measured vibration signals

Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.     

Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation

Energy separation algorithm is good at tracking instantaneous changes in frequency and amplitude of modulated signals, but it is subject to the constraints of mono-component and narrow band. In most cases, time-varying modulated vibration signals of machinery consist of multiple components, and have so complicated instantaneous frequency trajectories on time-frequency plane that they overlap in frequency domain. For such signals, conventional filters fail to obtain mono-components of narrow band, and their rectangular decomposition of time-frequency plane may split instantaneous frequency trajectories thus resulting in information loss. Regarding the advantage of generalized demodulation method in decomposing multi-component signals into mono-components, an iterative generalized demodulation method is used as a preprocessing tool to separate signals into mono-components, so as to satisfy the requirements by energy separation algorithm. By this improvement, energy separation algorithm can be generalized to a broad range of signals, as long as the instantaneous frequency trajectories of signal components do not intersect on time-frequency plane. Due to the good adaptability of energy separation algorithm to instantaneous changes in signals and the mono-component decomposition nature of generalized demodulation, the derived time-frequency energy distribution has fine resolution and is free from cross term interferences. The good performance of the proposed time-frequency analysis is illustrated by analyses of a simulated signal and the on-site recorded nonstationary vibration signal of a hydroturbine rotor during a shut-down transient process, showing that it has potential to analyze time-varying modulated signals of multi-components.     

Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.     

基于听觉模型与自适应分数阶Fourier变换的声学特征在语音识别中的应用

分数阶Fourier变换在处理非平稳信号尤其是chirp信号方面有着独特的优势,而人耳听觉系统具有自动语音识别系统难以比拟的优良性能。本文采用Gammatone听觉滤波器组对语音信号进行前端时域滤波,然后对输出的各个子带信号用分数阶Fourer变换方法提取声学特征。分数阶Fourier变换的阶数对其性能有着重要影响,本文针对子带时域信号提出了采用瞬时频率曲线拟合求取阶数的方法,并将其与采用模糊函数的方法作了比较。在干净与含噪汉语孤立数字库上的语音识别结果表明,采用新提出的声学特征得到的识别正确率相对MFCC基线系统有了显著提高;根据瞬时频率曲线搜索阶数的算法与模糊函数方法相比,计算量大大减少,并且根据该方法提取的声学特征得到了最高的平均识别正确率。      

多速率STFT超宽带信号瞬时频率估计研究

提出多速率短时傅里叶变换(Multi Rate Short Time Fourier Transform,MR-STFT)瞬时频率估计算法,提高了超宽带信号瞬时频率估计精度。该方法将多速率信号处理算法与短时傅里叶变换(STFT)技术相结合,兼顾采样频率和被测频率,将宽频范围进行分段采样,对分段处理结果进行拟合,构成多速率STFT算法,实现超宽带信号瞬时频率的高精度测量。论文通过对仿真信号和实测信号进行处理,研究了方法的可行性和频率估计精度,结果表明MR-STFT算法较大提高了超宽带信号瞬时频率估计精度,尤其对低信噪比的超宽带信号效果显著。     

改进的基于Spatial WVD和Hough变换的近场信源参量估计及性能分析

利用近场信源的一次快拍数据具有近似线性调频信号形式这一特点,将空域—频域联合平滑WVD和边缘检测以及直线拟合技术结合,对边缘检测结果进行Hough变换得到候选点集,并采用最小二乘聚类拟合获得抑制交叉项与噪音影响的初始频率和调频率估计,进而估计出近场信源的波达角和距离参量.理论分析和仿真结果表明,该方法能够显著抑制WVD分布的交叉项,正确拟合信源的原始空域—频域分布,并具有较低的运算复杂度.     

Acceleration target detection based on LFM radar

In radar systems, the echo signal caused by an accelerated target can be similarly considered as linear frequency modulation (LFM) signal. In high signal-to-noise ratio (SNR), discrete polynomial-phase transform (DPT) algorithm can be used to detect the echo signal, as it has low computation complexity and high real-time performance. However, in low SNR, the DPT algorithm has a large mean square error of the rate of frequency modulation and a low detection probability. In order to detect LFM signal in low SNR, this paper proposes a detection method, segment discrete polynomial-phase transform (SDPT), which means, at first, dividing the whole echo pulses into several segments with same duration in time domain, and then, using coherent accumulation method of DFT to segments, at last, processing this signal with DPT in intra-segment. In the case of a large number of segments, the SDPT can improve the output SNR. In addition, in a certain SNR, to the target signal with big sampling interval, large acceleration and less segments, this paper proposes an algorithm to detect the LFM signal generated from the combination of an improved DPT (IDPT) and fractional Fourier transform (FRFT). The output SNR of this algorithm is connected with the length of time delay. In the simulation, when the length of the time delay is 0.2 N, the output SNR is 2.5 dB more than that which results from directly using DPT. Finally, the detection performance and algorithm complexity of the proposed algorithm were analyzed, and the simulated and measured data verify the effectiveness of the algorithm.     

基于希尔伯特变化的微小振动激光多普勒信号处理

为了实现对固体目标微小振动参数的测量,建立了微小振动的激光多普勒信号模型。采用希尔伯特数字运算,将激光多普勒振动信号的即时信号采样转化为信号的谱采样。通过频谱计算得到每个振动周期中瞬时频率的平均数,应用差值采样序列积分计算得到振动频率,最后根据振动信号频率变化与振幅的关系得到振幅。采用希尔伯特方法对实验测试结果进行处理验证,并分析了误差来源。实验结果表明：实验测量目标的振动振幅约为1.85×10-4m,转动的圆频率约为170 Hz。因此,应用希尔伯特变换方法处理测量的目标微小振动信号,获取目标运动的参数是可行的。     

分数傅里叶变换滤波优化算法及滤波器设计

分数傅里叶变换（FrFT, fractional Fourier transform）是经典傅里叶变换的一种表现形式，可理解为在时 频平面中坐标轴系以原点为轴逆时针旋转一定的角度。通过数学推导，对能否利用分数傅里叶变换进行信号滤波，滤波的优化算法如何，以及滤波器有哪些设计结构等问题进行深入的研究，指出分数傅里叶变换适用于非平稳信号滤波。采用Matlab进行了数值仿真实验。实验结果表明：在信号滤波方面，由于傅里叶变换在处理某些数据时有局限性，因此分数傅里叶变换与傅里叶变换相比具有显著的优势。最后给出FrFT滤波器的设计思想。     

The concise fractional Fourier transform and its application in detection and parameter estimation of the linear frequency-modulated signal

A concise fractional Fourier transform(CFRFT) is proposed to detect the linear frequency-modulated(LFM) signal with low signal to noise ratio(SNR).The frequency axis in time-frequency plane of the CFRFT is rotated to get the spectrum of the signal in different angles using chirp multiplication and Fourier transform(FT).For LFM signal which distributes as a straight line in time-frequency plane,the CFRFT can gather the energy in the corresponding angle as a peak and improve the detection SNR,thus the LFM signal of low SNR can be detected.Meanwhile,the location of the peak value relates to the parameters of the LFM signal.Numerical simulations and experimental results show that,the proposed method can be used to efficiently detect the LFM signal masked by noise and to estimate the signal's parameters accurately.Compared with the conventional fractional Fourier transform(FRFT),the CFRFT reduces the transform complexity and improves the real-time detection performance of LFM signal.     

低信噪比线性调频信号目标的方位估计

线性调频(LFM)信号目标的方位估计是水声探测研究的重要内容,在进行方位估计时,若存在强干扰信号源与强背景噪声,阵元接收信号的信噪比会显著降低,严重影响LFM信号目标方位估计结果的准确性.针对该问题,提出了一种简明分数阶滤波方法,并将其与常规波束形成方法(CBF)相结合来实现低信噪比条件下LFM信号目标的方位估计.简明分数阶傅里叶变换能在正交角度上将LFM信号的能量聚集在特定频点处并形成明显的能量峰,利用该特性,可对阵列各阵元接收的低信噪比LFM信号在简明分数阶域聚集的能量峰进行最佳滤波,以滤除干扰信息及背景噪声.对滤波输出进行逆简明分数阶傅里叶变换可得到增强信干比和信噪比的阵元域信号,进一步用于目标方位估计,就能获得更加准确的目标方位。数值仿真结果和海试实验数据处理结果验证表明,本文所提出的方法可有效抑制干扰和背景噪声,并对低信噪比LFM信号进行准确、稳健的方位估计。      

简明分数阶傅里叶变换及其对线性调频信号的检测和参数估计

针对低信噪比下线性调频信号的检测问题,提出了一种简明分数阶傅里叶变换方法。该变换借助chirp相乘和傅里叶变换对时频平面上的频率轴进行旋转,以获取信号在各个角度下频率轴上的频谱分布。对时频分布呈直线状的线性调频信号,简明分数阶傅里叶变换能在特定角度上将信号能量聚集成尖锐的强能量峰,从而提高信噪比,实现对线性调频信号的可靠检测和参数估计。数值仿真和实验验证结果表明,简明分数阶傅里叶变换可对较低信噪比的线性调频信号实现有效检测,并由变换域峰值的位置对信号参数进行准确估计。相比于传统的分数阶傅里叶变换方法,简明分数阶傅里叶变换的复杂度更低,离散计算效率更高,在对噪声掩盖下的线性调频信号进行检测和参数估计时能更好地满足实时处理的要求。      

Spectrum measurement in the fractional Fourier domain

Fractional Fourier transform (FRFT) plays an important role in many fields of optics and signal processing. This paper considers the problem of real-time measurement of the spectrum of a signal in the FRFT domain. In this paper, we propose two approaches for real-time measurement of the FRFT of a signal based on modulation and bandpass filtering systems. The relation is established between the linear frequency modulation (LFM or chirp) spectrum and the FRFT of its envelope. In addition, two applications for spectrum measurement are presented in the FRFT domain. The LFM signal can be bandlimited in the Fourier transform (FT) domain through spectrum measurement associated with bandpass filtering method. The results can also be useful in the problems related to swept-frequency filter for measurement in the FRFT domain.     

Application of Hilbert–Huang transform to laser Doppler velocimeter

A new method based on Hilbert–Huang transform is proposed to analyze the laser Doppler signal with a large acceleration. The Doppler signal is decomposed into several Intrinsic Mode Functions (IMFs) via empirical mode decomposition (EMD). And the Hilbert transform is used to compute the instantaneous frequency. The vehicle velocity parameter is estimated by taking linear fitting on the instantaneous frequency of the relevant IMF. The simulation results show that the HHT-based method is quite useful for the LDV that offers velocity parameter to the vehicle self-contained navigation system when the vehicle moves at a large acceleration.     

基于时变功率谱的激光器调谐瞬时线宽测量方法研究

半导体激光器的线宽通常采用激光外差测量技术,通过差拍信号的功率谱密度函数来确定,受傅里叶变换方法的限制,得到的均是在一定时间段内的静态平均线宽。为了获得半导体激光器在电流调谐过程中的瞬时线宽特性,提出了利用时变功率谱获知调谐瞬时线宽的相干和非相干测量方法,并分别进行了理论分析和实验验证。首先对半导体激光器输出光信号及差拍信号进行了时间-频率域下的数学描述,确定了时变功率谱与调谐瞬时线宽的关系;其次,针对差拍信号的趋向性特征,提出了趋势局部均值分解方法,并研究了利用分解出的乘积函数建立差拍信号及激光器输出光信号的时变功率谱的方法;最后利用非相干和相干测量法分别获得了分布反馈式半导体激光器在50~51及50~100 mA锯齿波电流调谐过程中的瞬时线宽。     

THE APPLICATION OF PSEUDO-PHASE PORTRAIT IN MACHINE CONDITION MONITORING

This paper presents a robust phase space reconstruction method based on singular value decomposition technique and its applications to large rotating machine and gear system condition monitoring and fault diagnosis. The singular value decomposition is used to determine the effective embedding space and to reduce the noise level of a measured vibration signal. Following the singular value decomposition, a pseudo-phase portrait can be obtained in the effective embedding space. This pseudo-phase portrait is then used to extract qualitative features of machine faults. Experience has shown that when one compares the pseudo-phase portraits obtained under different machine conditions, it is often possible to detect major differences due to different dynamic and kinematic mechanisms. In the case of gear system condition monitoring, correlation dimension has been introduced to evaluate these differences in order to obtain more accurate and reliable diagnosis. The pseudo-phase portrait is conceptually simple and has been found to be sensitive to some fault types. It is promising therefore that such pseudo-phase portraits can be used to realize real-time, online computer-aided diagnosis of machine faults.     

Improved space time prewhitener for linear frequency modulation reverberation using fractional Fourier transform

This letter presents an improved space time prewhitening method for linear frequency modulation (LFM) reverberation. The proposed method transforms the reverberation to fractional Fourier domain to whiten using fractional Fourier transform. The linear varying frequency in LFM reverberation is focused on a stationary frequency, and the adjacent block signal is used as the reference signal of prewhitening. Finally, experiment results with real reverberation data verify that the proposed method improves the detection performance of active sonar in shallow sea significantly.     

Localization of targets in CW-LFM radars with combined modulating signal

An improved algorithm of detecting multiple targets by cw radars with linear frequency modulation (LFM) is presented. A combined modulating signal consisting of successive LFM and pure Doppler periods has been used. Processing of pure Doppler periods does not require large computational resources and the obtained results are more accurate. This construction of algorithm allows reducing the probability of false alarm in the localization regime and simplifying the processing in the detection regime.