基于改进Hilbert-Huang变换的管路环向裂纹故障诊断研究

在制造、加工、装配以及服役过程中,航空液压管路可能存在平面型缺陷。在适航载荷作用下,这些缺陷会萌生裂纹并发生扩展,最终导致管路破裂和油液泄漏。为了在管路产生微裂纹时对其进行故障诊断,采用了自行搭建的航空液压管路模态试验平台和测控系统,对存在环向裂纹航空液压管路的振动信号进行了采集和预处理。而在对管路振动信号进行分解时,由于经验模态分解(Empirical Mode Decomposition,EMD)算法存在模态混叠问题难以获得准确的本征模态函数(Intrinsic Mode Functions,IMF)。为提高信号处理精度,提出一种基于自适应噪声完备经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)和Hilbert变换理论相结合的改进Hilbert-Huang变换方法对航空液压管路环向裂纹进行故障诊断。研究结果表明,该方法能够更准确地诊断航空液压管路的环向裂纹故障。

Optimization of the End Effect of Hilbert-Huang transform(HHT)

In fault diagnosis of rotating machinery, Hil- bert-Huang transform （HHT） is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF （Intrinsic Mode Func- tion）. To counter such problems in HHT, a new method is put forward to process signal by combining the general- ized regression neural network （GRNN） with the bound- ary local characteristic-scale continuation （BLCC）. Firstly, the improved EMD （Empirical Mode Decompo- sition） method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the gen- erated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain, frequency domain and related parameters of Hilbert- Huang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and 27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method caneffectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accuratelX.

基于Hilbert-Huang Transform的心音信号谱分析

心音信号是一种典型的非平稳信号 ,传统信号处理方法的应用受到很大限制。针对此本文提出了基于Hilbert-HuangTransform(HHT)的心音信号的分析方法 ,对冠心病患者的心音信号进行了分析。通过把心音信号分解为内蕴模式函数 ,利用Hilbert变换建立了心音信号的时间 -频率 -能量三维Hilbert谱分布以及边界谱分布 ;Hilbert谱及其边界谱在时域以及频域以较高的分辨率表征了心音信号的时频变化特性 ,揭示了冠心病患者心音信号的病理特征 ;为冠心病的早期无损诊断奠定了坚实基础 ,临床实践中有较大的指导价值。

Efficacy of Hilbert-Huang Transform (HHT) in the Analysis of Instantaneous Low Frequency Waves of Magnetosheath

The flow of supersonic plasma is accompanied by a highly thermalized region called the Magnetoshealth found after the bow shock. Enclosed within this region are different wave modes associated with classes of boundaries which have been determined by different methods. The efficacy of Hilbert-Huang transform (HHT) is based on the conditionality of allowing for the local analysis of frequencies, which presents the physical meaning of the original signal at that instant. The observed data have been taken from Cluster II Fluxgate Magnetometer (FGM) instrument that provides advantage for the analysis in three dimensions. The result compares favourably with instantaneous frequencies computed using simple Hilbert transform (SHT) with electric field measurements of Cluster II mission already carried out in literatures. The result of this study has shown that HHT provides the best applicability in the magnetosheath data analysis than the wavelet and Fast Fourier Transform (FFT). The application of HHT based on its advantages over other methods is viewed to be very critical in the analysis of multi-frequency signals where different frequencies could be determined distinctively at a point.

Efficacy of Hilbert-Huang Transform (HHT) in the Analysis of Instantaneous Low Frequency Waves of Magnetosheath

The flow of supersonic plasma is accompanied by a highly thermalized region called the Magnetoshealth found after the bow shock. Enclosed within this region are different wave modes associated with classes of boundaries which have been determined by different methods. The efficacy of Hilbert-Huang transform (HHT) is based on the conditionality of allowing for the local analysis of frequencies, which presents the physical meaning of the original signal at that instant. The observed data have been taken from Cluster II Fluxgate Magnetometer (FGM) instrument that provides advantage for the analysis in three dimensions. The result compares favourably with instantaneous frequencies computed using simple Hilbert transform (SHT) with electric field measurements of Cluster II mission already carried out in literatures. The result of this study has shown that HHT provides the best applicability in the magnetosheath data analysis than the wavelet and Fast Fourier Transform (FFT). The application of HHT based on its advantages over other methods is viewed to be very critical in the analysis of multi-frequency signals where different frequencies could be determined distinctively at a point.

基于HHT的绝缘子泄漏电流分析及放电状态分类识别

泄漏电流是污秽绝缘子在线监测参数,能动态地反映绝缘子表面的放电状态。文中开展了瓷绝缘子人工污秽放电试验,利用Hilbert-Huang变换分析了不同污闪阶段的泄漏电流固有模态函数分量、Hilbert边际谱与时频熵,从时频域及波形细节提取了15个特征量,使用主成分分析法与最小二乘支持向量机分类器对污秽放电状态进行识别。结果表明:起始放电阶段与闪络阶段的泄漏电流固有模态函数分量较多;泄漏电流的Hilbert边际谱上频率主要分布在0~150 Hz、200~250 Hz范围内;闪络前泄漏电流的时频熵值总是大于闪络后的;当训练样本数为测试样本数5倍及以上时,分类器的综合评判准确率可达99%,准确实现了污秽放电状态的分类识别。文中研究结果可为建立绝缘子污闪预警系统提供依据。

基于IHHT‑RF的配电网单相接地故障选线方法

小电流系统发生单相接地故障时故障特征易受高接地过渡电阻、小初相角等弱故障条件影响而导致选线准确率低。为此,提出一种基于改进希尔伯特黄变换—随机森林(improved Hilbert⁃Huang transform⁃random forest,IHHT⁃RF)的配电网单相接地故障选线方法。首先,提取每条线路在故障发生时的电流暂态信号,通过IHHT提取纯净的暂态电气量,构造标准差、能量熵和幅值畸变度3类特征向量;然后,将特征向量输入RF分类器建立故障选线模型,把故障选线问题转化为二分类问题;最后,将测量数据输入RF分类器中得出分类结果,实现故障线路的自动识别。仿真结果表明,该选线方法综合利用暂态信号的幅值、频率和能量等特征信息,不受弱故障条件、馈线结构等因素的影响,能有效提高故障选线的准确率,具有较强的适应性和可靠性。

Practical implementation of Hilbert-Huang Transform algorithm

Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better than MPM for the signal that has strong asymmetric waveform. However, it has to perform extrema envelope extending in every shifting process.

A technique to improve the empirical mode decomposition in the Hilbert-Huang transform

The Hilbert-based time-frequency analysis has promising capacity to reveal the time-variant behaviors of a sys- tem.To admit well-behaved Hilbert transforms,component decomposition of signals must be performed beforehand.This was first systematically implemented by the empirical mode decomposition(EMD)in the Hilbert-Huang transform,which can provide a time-frequency representation of the signals.The EMD,however,has limitations in distinguishing different components in narrowband signals commonly found in free-decay vibration signals.In this study,a technique for decompo- sing components in narrowband signals based on waves' beating phenomena is proposed to improve the EMD,in which the time scale structure of the signal is unveiled by the Hilbert transform as a result of wave beating,the order of component ex- traction is reversed from that in the EMD and the end effect is confined.The proposed technique is verified by performing the component decomposition of a simulated signal and a free decay signal actually measured in an instrumented bridge structure.In addition,the adaptability of the technique to time-variant dynamic systems is demonstrated with a simulated time-variant MDOF system.

Analysis of the characteristics of gastrointestinal motility based on Hilbert-Huang transform method

Pressure activity data as an important index of gastrointestinal(GI)motility can be obtained from thewireless radiotelemetry capsule.The Hilbert-Huang transform(HHT)method,whieh is more effective toprocess non-stationary signal,is proposed to identify the characteristics of GI motility.We decompose thepressure activity data into intrinsic mode functions(IMFs),calculate the Hilbert marginal spectrum andattain the peristalsis characteristics of GI tract.The IMFs represent the peristalses modes of GI tract activ-ity embedded in the pressure data.The time-varying characteristic of the method suggests that the HI-IT issuitable to aeeommodate other non-stationary biomedical data analysis.

Application of Hilbert-Huang transform to denoising in vortex flowmeter

Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies which indicate volumetric flowrate from noisy data, especially at low flowrates. Hilbert-Huang transform was adopted to estimate vortex frequency. The noisy raw signal was decomposed into different intrinsic modes by empirical mode decomposition, the time-frequency characteristics of each mode were analyzed, and the vortex frequency was obtained by calculating partial mode’s instantaneous frequency. Experimental results show that the proposed method can estimate the vortex frequency with less than 2% relative error; and in the low flowrate range studied, the denoising ability of Hilbert-Huang transform is markedly better than Fourier based algorithms. These findings reveal that this method is accurate for vortex signal processing and at the same time has strong anti-disturbance ability.

Structural modal parameter identifi cation and damage diagnosis based on Hilbert-Huang transform

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.

Application of Hilbert-Huang Transform to the Analysis of the Landslides Triggered by the Wenchuan Earthquake

A steep rock hill with two side slopes located at DK30+256 of National Road 213 was used as a prototype for analysis. The full process from initial deformation to sliding of the slope during ground shaking was simulated by using a new Continuum-based Discrete Element Method. During the earthquake, when shaking amplitudes were lower,the stress concentration points firstly appeared at the top of the slip mass, and then some tension failure points appeared, followed by shear failure points. At the same time, both the instantaneous frequencies of accelerations in the bedrock and that in the slip mass basically stayed in two different ranges. The energy transmittance coefficients of the sliding surface also stayed in a high range. As the ground shaking lasted,the number of failure points gradually increased until landslide occurrence. The instantaneous frequencies of accelerations in the slip mass and the energy transmittance coefficients of sliding surface gradually decreased, and both finally converged to a lower range. And then, the reasons triggering landslides are analysis in the joint time-frequency domain using Hilbert-Huang Transform, as follows: the differences of distribution and dissipation of the earthquake energy and the inconsistency of movements between the slip mass and the bedrock were the two major influence factors.

Bark-Wavelet Analysis and Hilbert -Huang Transform for Underwater Target Recognition

Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approach which consists of the algorithms of Bark-wavelet analysis, Hilbert-Huang transform and support vector machine is proposed. The performance of the proposed method is validated by comparing with traditional method and evaluated by the recognition experiments for SNRs of 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.The results show that the average recognition rate of the method is above 88% and can be increased by 0.75 % to 6.25% under various SNR conditions compared to the baseline system.

Modal identification based on Hilbert-Huang Transform of structural response with S VD preprocessing

In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.

Mode Recognition of Lamb Wave Detecting Signals in Metal Plate Using the Hilbert-Huang Transform Method

The dispersion and multiple modes characteristics which exist in the propagation of Lamb waves (LW) in metal plates make it extremely hard to analyze and recognize the detection echo signals of defects. As a newly developed time-frequency analysis method in recent years, Hilbert-Huang transform (HHT) is one of the powerful tools to analyze non-stationary signals. The experimental LW detecting system for single aluminum plate is setup in this work, and the LW detecting signals are analyzed by HHT. The overlapped LW detecting signals of different modes are recognized by the means of extracting flight time of intrinsic mode functions (IMFs) after Hilbert transform (HT). The experiment results, agreeing well with the theoretical analysis, indicate that the HHT method can clearly recognize overlapped LW detecting signals of different modes in metal plates, but could hardly recognize that of the same mode. HHT can be an effective method to recognize LW detecting signals of different modes in metal plates.

基于HHT-LSTM的冬奥会临时设施运行趋势预测方法研究

针对冬奥会延庆赛区临时设施的安全性和可使用性,本文充分结合信号处理算法与深度神经网络,提出了一种由希尔伯特黄变换(Hilbert-Huang transform,HHT)对时序数据进行信号分解和信号特征提取,长短期记忆网络(long short-term memory,LSTM)进行临时设施运行趋势预测2部分构成模型。该模型基于受严寒天气和大客流诱发的看台振动等一系列外因影响所测得的真实振动和倾角数据,实现对设施进行有效的预测,以避免发生安全问题,解决了由于受数据中一些无关特征因素的干扰导致预测准确度低的问题。论文提出的方法与循环神经网络(recurrent neural network,RNN)、门控循环网络(gated recurrent neural network,GRU)、双向RNN和双向GRU等运行趋势预测方法进行比较,验证了本文方法的可行性和有效性,实验结果也说明所提出的模型在此类任务中表现非常出色。

Pulse Wave Analysis in Patients with Coronary Heart Disease Based on Hilbert-Huang Transformation and Time-domain

Objective: To study the characteristics of pulse tracings in CHD, and objectively evaluate the significance of pulse signal in diagnosis and appreciation of therapeutic effect in patients with coronary heart disease(CHD), and accordingly provide with theoretic proofs for developing non-invasive technique of pulse diagnosis. Methods: By using the pulse detection system, pulse graphs in CHD patients, patients without CHD and "health" adults were collected and compared. Then characters of the pulse signal were analyzed with Hilbert-Huang transformation routine (HHT) and time-domain method respectively. Results: There existed characteristic change in pulse graph in CHD. ① h1,h3,h4,h3/h1,t,t5/t4 in time domain parameters of pulse graph increased and w1 was widened. ② 44% of C2 wave in HHT display showed chaotic and disorderly wave and irregularly wave amplitude in CHD. And 72% of C5 Wave exhibited in irregular wave with average wave amplitude over 10 gram-forces. These changes were significantly different from health adults. Conclusion: Characteristic wave of pulse graph extracted with methods of time domain or HHT routine might be considered as proofs for diagnosis and differentiation in CHD. Our researches prognosticate that pulse diagnosis can be used as an ancillary determination in occurrence of CHD for reasons of the advantage of convenient operation and non-invasion.

Distortion Identification Technique Based on Hilbert-Huang Transform in Video Stabilization

A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified simply based on Hilbert marginal spectral analysis after empirical mode decomposing. And distortion frequency is identified by analyzing the occurrence frequency of instantaneous frequency components of every intrinsic mode functions. Rational digital frequency filter with suitable cutoff frequency is designed to remove undesired fluctuations based on identification results. Experimental results show that this technique can identify distortion model and distortion frequency of displacement sequence accurately and efficiently. Based on identification results, distorted image sequence can be stabilized effectively.

Hilbert Huang Transform for Predicting Proteins Subcellular Location

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for the understanding the mechanism of programmed cell death, and their function is related to their types. The apoptosis proteins are categorized into the following four types: (1) Cytoplasmic protein;(2) Plasma membrane-bound protein;(3) Mitochondrial inner and outer proteins;(4) Other proteins. A novel method, the Hilbert-Huang transform, is applied for predicting the type of a given apoptosis protein with support vector machine. High success rates were obtained by the re-substitute test (98/98=100%), jackknife test (91/98 = 92.9%).