A Recursive Method of Time-Frequency Analysis for the Signal Processing of Flutter Test with Progression Variable Speed

Focused on the non-statlonarity and real-time analysis of signal in flutter test with progression variable speed （FTPVS）, a new method of recursive time-frequency analysis is presented. The time-varying system is tracked on-line by building a time-varying parameter model, and then the relevant parameter spectrum can be obtained. The feasibility and advantages of the method are examined by digital simulation. The results of FTPVS at low-speed wind-tunnel promise the engineering application perspective of the method.

Wavelet analysis and its application to signal processing

The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.

Physiological signal processing in heart rate variability measurement:A focus on spectral analysis

Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under conditions of rest or work,the temporal distances of successive heartbeats are subject to fluctuations,thereby forming the basis of Heart Rate Variability(HRV).In normal conditions,the human is persistently exposed to highly changing and dynamic situational demands.With these demands in mind,HRV can,therefore,be considered as the human organism’s ability to cope with and adapt to continuous situational requirements,both physiologically and emotionally.Fast Fourier Transform(FFT)is used in various physiological signal processing,such as heart rate variability.FFT allows a spectral analysis of HRV and is great help in HRV analysis and interpretation.

Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation

In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the esti- mated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.

Fractional Envelope Analysis for Rolling Element Bearing Weak Fault Feature Extraction

The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring. Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction. The effectiveness of the proposed method is verified through simulation signal and experiment data. © 2017 Chinese Association of Automation.

Fast and robust strain signal processing for aircraft structural health monitoring

This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis method,which can be regarded as a mixture of Gaussian-like(tiny hairy signals)and impulse-like noise(single signals with anomalous movements in peak and valley areas).Based on this,a least squares filtering method is employed to preprocess strain signals.To precisely eliminate noise or outliers in strain signals,we propose a novel variational model to generate step signals instead of strain ones.Expert judgments are employed to classify the generated signals.Based on the classification labels,whether the aircraft is structurally healthy is accurately judged.By taking the generated step count vectors and labels as an input,a discriminative neural network is proposed to realize automatic signal discrimination.The network output means whether the aircraft structure is healthy or not.Experimental results demonstrate that the proposed scheme is effective and efficient,as well as achieves more satisfactory results than other peers.

Ultrasonic Nondestructive Signals Processing Based on Matching Pursuit with Gabor Dictionary

The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-domain methods have been partly successful in identifying small cracks, but not so successful in estimating crack size, especially in strong backscattering noise. Sparse signal representation can provide sparse information that represents the signal time-frequency signature, which can also be used in processing ultrasonic nondestructive signals. A novel ultrasonic nondestructive signal processing algorithm based on signal sparse representation is proposed. In order to suppress noise, matching pursuit algorithm with Gabor dictionary is selected as the signal decomposition method. Precise echoes information, such as crack location and size, can be estimated by quantitative analysis with Gabor atom. To verify the performance, the proposed algorithm is applied to computer simulation signal and experimental ultrasonic signals which represent multiple backscattered echoes from a thin metal plate with artificial holes. The results show that this algorithm not only has an excellent performance even when dealing with signals in the presence of strong noise, but also is successful in estimating crack location and size. Moreover, the algorithm can be applied to data compression of ultrasonic nondestructive signal.

Feasibility of differentiating defect signals of ultrasonic testing for laser weld based on independent component analysis theory

Separating noise from observed signals was studied.When the small defect in the T-shape laser welding joint was inspected by ultrasonic testing system adopting independent component analysis(ICA) theory to process the signals.The principle of automatic ultrasonic testing signals processing and negentropy law of ICA were introduced.The experimental data were processed using relative analysis tools and results showed that the ICA could separate defects signals from noise effectively in laboratory.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.

Quantum-Classical Algorithm for an Instantaneous Spectral Analysis of Signals:A Complement to Fourier Theory

A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it’s based on Schrödinger’s equation. In the classical world, it is named frequency in time (FIT), which is used here as a complement of the traditional frequency-dependent spectral analysis based on Fourier theory. Besides, FIT is a metric which assesses the impact of the flanks of a signal on its frequency spectrum, not taken into account by Fourier theory and lets alone in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages, among others: 1) compact support with excellent energy output treatment, 2) low computational cost, O(N) for signals and O(N2) for images, 3) it does not have phase uncertainties (i.e., indeterminate phase for a magnitude = 0) as in the case of Discrete and Fast Fourier Transform (DFT, FFT, respectively). Finally, we can apply QSA to a quantum signal, that is, to a qubit stream in order to analyze it spectrally.

Mathematical theory of signal analysis vs. complex analysis method of harmonic analysis

We present recent work of harmonic and signal analysis based on the complex Hardy space approach.

Research progress of the fractional Fourier transform in signal processing

The fractional Fourier transform is a generalization of the classical Fourier transform, which is introduced from the mathematic aspect by Namias at first and has many applications in optics quickly. Whereas its potential appears to have remained largely unknown to the signal processing community until 1990s. The fractional Fourier transform can be viewed as the chirp-basis expansion directly from its definition, but essentially it can be interpreted as a rotation in the time-frequency plane, i.e. the unified time-frequency transform. With the order from 0 increasing to 1, the fractional Fourier transform can show the characteristics of the signal changing from the time domain to the frequency domain. In this research paper, the fractional Fourier transform has been comprehensively and systematically treated from the signal processing point of view. Our aim is to provide a course from the definition to the applications of the fractional Fourier transform, especially as a reference and an introduction for researchers and interested readers.

Survey on Encoding Schemes for Genomic Data Representation and Feature Learning——From Signal Processing to Machine Learning

Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical values for data representation and feature learning in various applications. Similar conversion occurs in Genomic Signal Processing(GSP), where genome sequences are transformed into numerical sequences for signal extraction and recognition. This kind of conversion is also called encoding scheme. The diverse encoding schemes can greatly affect the performance of GSP applications and machine learning models. This paper aims to collect,analyze, discuss, and summarize the existing encoding schemes of genome sequence particularly in GSP as well as other genome analysis applications to provide a comprehensive reference for the genomic data representation and feature learning in machine learning.

Time-synchronous-averaging-spectrum based on super-resolution analysis and application in bearing fault signal identification

Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted.This paper studies the mathematical properties of TSA,where three propositions are given to reveal the nature of TSA.This paper also proposes a TSA-spectrum based on super-resolution analysis and it decomposes a signal without using any base function.In contrast to discrete Fourier transform spectrum(DFT-spectrum),which is a spectrum in frequency domain,TSA-spectrum is a period-based spectrum,which can present more details of the cross effects between different periodic components of a quasiperiodic signal.Finally,a case study is carried out using bearing fault analysis to illustrate the performance of TSA-spectrum,where the rotation speed fluctuation of the shaft is estimated,which is about 0.12 ms difference.The extracted fault signals are presented and some insights are provided.We believe that this paper can provide new motivation for TSA-spectrum to be widely used in applications involving quasiperiodic signal processing(QSP).

Characteristics of acoustic emission signals in damp cracking coal rocks

A uniaxial load experiment on coal rocks at different stress rates was carried out, based on the characteristics of acoustic emission (AE) signals in cracking coal rocks, decomposition, de-noising and reconstruction for the AE signals through wavelet packet transform for solving the current problems created by the presence of noise in AE signals and the existing problems in AE signal processing. The results show that the various characteristics of AE signals in coal rocks cracking under different situations can be clearly reflected, after the AE signals are de-noised by the wavelet packet. Compared to dry coal rocks, the number of AE occurrences in damp coal rocks was significantly reduced, as well as the average amplitude. The number of AE occurrences in damp and dry coal rocks clearly increased with increases in the loading rate, but the largest amplitude of the AE signals in damp coal rocks has been reduced. There is no clear evidence of change in dry coal rocks.

Denoising Method for Shear Probe Signal Based on Wavelet Thresholding

Shear probe works under a tough environment where the turbulence signals to be measured are very weak. The measured turbulence signals often contain a large amount of noise. Due to wide frequency band, noise signals cannot be effectively removed by traditional methods based on Fourier transform. In this paper, a wavelet thresholding denoising method is proposed for turbulence signal processing in that wavelet analysis can be used for multi-resolution analysis and can extract local characteristics of the signals in both time and frequency domains. Turbulence signal denoising process is modeled based on the wavelet theory and characteristics of the turbulence signal. The threshold and decomposition level, as well as the procedure of the turbulence signal denoising, are determined using the wavelet thresholding method. The proposed wavelet thresholding method was validated by turbulence signal denoising of the Western Pacific Ocean trial data. The results show that the propsed method can reduce the noise in the measured signals by shear probes, and the frequency spectrums of the denoised signal correspond well to the Nasmyth spectrum.

Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.

Extraction of Signals Buried in Noise: Non-Ergodic Processes

In this paper, we propose extraction of signals buried in non-ergodic processes. It is shown that the proposed method extracts signals defined in a non-ergodic framework without averaging or smoothing in the direct time or frequency domain. Extraction is achieved independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Performances of the pro-posed extraction method and comparative results with other methods are demonstrated via experimental Doppler velocimetry measurements.

基于信号处理的液压泵故障检测方法研究综述

液压泵的故障诊断是其正常工作和健康管理的关键,基于信号处理的液压泵诊断方法已经成为主流。近年来,学者们对液压泵故障诊断的研究非常活跃,但对液压泵故障分析和诊断方法缺少系统的总结和分析。通过对液压泵相关文献进行统计分析,系统地总结了液压泵故障产生的原因、故障诊断的基本方法及研究进展,指出了液压泵故障分析和诊断领域的发展前景,为研究人员和相关维修人员提供了参考价值并指明了研究方向。

降速方式对混流泵压力脉动特性的影响

混流泵在降速过程中的内部流动特性变化对其性能和稳定性有重要影响。针对这一问题,该研究探究了混流泵在降速过程中内部流动的复杂性,尤其关注不同降速方式对泵内压力脉动特性的影响。通过构建多通道测试系统,收集了混流泵在不同降速方式下的外特性参数信号和泵内压力脉动信号。并采用非稳态信号处理技术,从时域、频域和时频三方面探究不同降速方式对叶轮出口和导叶内部的压力脉动特性的影响以及二者之间的相关性。结果表明,泵内压力脉动的峰值和样本熵的变化趋势与不同降速方式的幂指数呈正相关关系;在整个降速过程中叶轮出口压力脉动对导叶内压力脉动起主导作用;叶轮出口和导叶内部压力脉动之间的相干频率主要集中于80~120 Hz之间;在不同降速过程中叶轮出口和导叶内部的压力脉动之间强相关区域的频带范围保持不变,但是强相关区域在整个降速过程频带范围中的占比有所变化;对于具有固定初始转速和目标转速的降速过程,降速方式的改变不会影响叶轮出口和导叶内压力脉动的相干频率。研究结论可为揭示混流泵瞬态运行特性及提高混流泵降速运行中的稳定性提供参考。