基于声信号递归Hilbert变换的轴承故障诊断研究

轴承缺陷检测与损伤程度检测一直是旋转机械领域内非常重视的问题,虽然目前针对振动信号的研究已经取得相当好的结果,但是对于难以安装振动传感器的情况,诊断效果仍需改进。针对强背景噪声下故障轴承产生的声音,提出一种基于递归Hilbert变换和一维卷积神经网络的诊断方法来提取抽象特征并进行模式识别。卷积神经网络结构中引入了全局平均池化层来加速网络的运行。最后,通过数据集验证了所提方法的有效性,与其他常用分类方法进行对比,验证了该方法的优越性。结果表明:所提算法不仅能够准确识别轴承的损伤部位,而且能够准确区分部件的损伤程度。

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。

Two-dimensional nanomaterials confined single atoms: New opportunities for environmental remediation

Two-dimensional(2D)supports confined single-atom catalysts(2D SACs)with unique geometric and electronic structures have been attractive candidates in different catalytic applications,such as energy conversion and storage,value-added chemical synthesis and environmental remediation.However,their environmental appli-cations lack of a comprehensive summary and in-depth discussion.In this review,recent progresses in synthesis routes and advanced characterization techniques for 2D SACs are introduced,and a comprehensive discussion on their applications in environmental remediation is presented.Generally,2D SACs can be effective in catalytic elimination of aqueous and gaseous pollutants via radical or non-radical routes and transformation of toxic pollutants into less poisonous species or highly value-added products,opening a new horizon for the contami-nant treatment.In addition,in-depth reaction mechanisms and potential pathways are systematically discussed,and the relationship between the structure-performance is highlighted.Finally,several critical challenges within this field are presented,and possible directions for further explorations of 2D SACs in environmental remediation are suggested.Although the research of 2D SACs in the environmental application is still in its infancy,this review will provide a timely summary on the emerging field,and would stimulate tremendous interest for designing more attractive 2D SACs and promoting their wide applications.

基于Hilbert变换的条纹-灰度线性投影三维测量

针对传统的条纹投影测量中绝对相位求解方法存在投影图像数量较多以及算法复杂程度高的问题,提出了一种基于Hilbert变换的条纹-灰度线性投影策略及相位求解方法,该方法仅使用3幅投影图像——1幅高频条纹图和2幅灰度线性变化图,通过Hilbert变换求得包裹相位,采用灰度线性变化图像得到基础相位,联立包裹相位和基础相位可得到条纹级数,进而得到绝对相位。通过构建的主动双目系统得到的三维重构结果发现,该方法可以有效恢复被测物体的三维形状,测量精度可达0.1 mm。

Two-dimensional plane strain consolidation of unsaturated soils considering the depth-dependent stress

In practical engineering,the total vertical stress in the soil layer is not constant due to stress diffusion,and varies with time and depth.Therefore,the purpose of this paper is to investigate the effect of stress diffusion on the two-dimensional(2D)plane strain consolidation properties of unsaturated soils when the stress varies with time and depth.A series of semi-analytical solutions in terms of excess pore air and water pressures and settlement for 2D plane strain consolidation of unsaturated soils can be derived with the joint use of Laplace transform and Fourier sine series expansion.Then,the inverse Laplace transform of the semi-analytical solution is given in the time domain using a self-programmed code based on Crump’s method.The reliability of the obtained solutions is proved by the degeneration.Finally,the 2D plots of excess pore pressures and the curves of settlement varying with time,considering different physical parameters of unsaturated soil stratum and depth-dependent stress,are depicted and analyzed to study the 2D plane strain consolidation properties of unsaturated soils subjected to the depthdependent stress.

Transforming a Two-Dimensional Layered Insulator into a Semiconductor or a Highly Conductive Metal through Transition Metal Ion Intercalation

Atomically thin two-dimensional(2D) materials are the building bricks for next-generation electronics and optoelectronics, which demand plentiful functional properties in mechanics, transport, magnetism and photoresponse.For electronic devices, not only metals and high-performance semiconductors but also insulators and dielectric materials are highly desirable. Layered structures composed of 2D materials of different properties can be delicately designed as various useful heterojunction or homojunction devices, in which the designs on the same material(namely homojunction) are of special interest because preparation techniques can be greatly simplified and atomically seamless interfaces can be achieved. We demonstrate that the insulating pristine ZnPS_3, a ternary transition-metal phosphorus trichalcogenide, can be transformed into a highly conductive metal and an n-type semiconductor by intercalating Co and Cu atoms, respectively. The field-effect-transistor(FET) devices are prepared via an ultraviolet exposure lithography technique. The Co-ZnPS_3 device exhibits an electrical conductivity of 8 × 10^(4) S/m, which is comparable to the conductivity of graphene. The Cu-ZnPS_3 FET reveals a current ON/OFF ratio of 1-05 and a mobility of 3 × 10^(-2 )cm^(2)·V^(-1)·s^(-1). The realization of an insulator, a typical semiconductor and a metallic state in the same 2D material provides an opportunity to fabricate n-metal homojunctions and other in-plane electronic functional devices.

基于Hilbert谱的行星齿轮箱故障诊断应用研究

行星齿轮箱以其诸多的传动优点广泛应用在双馈风力发电机组中,行星齿轮箱因其特殊的结构导致振动信号传递路径复杂和多路径调制特点。当行星齿轮箱的太阳轮、齿圈、行星轮关键部件出现局部故障时,频谱不能清晰地呈现部件损伤的特征频率,且频谱容易被噪音淹没。本文提出了一种Hilbert变换谱,把低频故障信号从高频的载波信号中解调出来,通过与理论计算的故障特征频率进行对比,可精确地对行星齿轮箱的故障进行定位。结果表明,通过Hilbert解调的低频故障频率能精确和部件理论计算特征频率一一对应,对故障进行准确定位,Hilbert变换在行星齿轮箱故障诊断中优势明显。

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。

基于自适应VMD-Hilbert的球磨机负荷参数预测

提出一种自适应变分模态分解(VMD)—希尔伯特(Hilbert)边际谱样本熵和最小二乘支持向量机(LSSVM)提取球磨机振动信号组合特征并预测负荷参数的方法。首先,使用自适应VMD的振动敏感信号调制来分解振动,得到反映振动特性的本征模态函数(IMF)分量;之后,进行Hilbert变换,得到边际谱样本熵;最后,将其作为输入特征向量输入到LSSVM,实现球磨机负荷参数预测。试验结果表明:该方法可以有效地提取球磨机的非线性不稳定的信号特征,较为精确地预测球磨机负荷参数。

阶次跟踪和Hilbert变换在滚动轴承非平稳信号故障诊断中的应用

风力发电机组传动链因风速的时变特性而处于变转速运行状态,其主轴承因低速、重载导致振动信号具有非平稳和调制特点,常规分析时频谱会出现“模糊”现象,不能及早提取故障特征频率。提出了阶次跟踪和Hilbert变换联合分析方法,使用阶次跟踪在角度域进行等角度重采样,把时域非平稳信号转换成角度域平稳信号,然后应用Hilbert变换把低频故障信号从高频载波信号中解调出来,以及早发现隐患。结果表明,阶次跟踪和Hilbert变换联合分析能够较早地提取出故障特征频率并对故障进行精确定位,对低速重载风电主轴承故障诊断十分有效。

Ultrasonic transducer array tomography for detecting defect of metal plate based on Hilbert transformation

Hilbert transformation and improved ellipse localization method is applied in ultrasonic transducer array tomography to detect defect of metal plate.By combining the improved ellipse localization method and time-reversal method,the new ultrasonic tomography algorithm employs smooth Hilbert envelope instead of discrete amplitude to reconstruct defect image.An ultrasonic tomography system with six transducers is built to evaluate the effectiveness of the new ultrasonic tomography algorithm.The S0 mode Lamb wave is excited on special condition,and the mode of received signal is identified by Vigner-Wille distribution.The gray value of image area is defined by envelope of the reflected S0 mode Lamb wave signal from defect boundary.Defect image can be reconstructed by summing gray value of all pixels in the image area.The experimentally reconstructed defect image shows that the new tomography algorithm based on Hilbert transformation is efficient for defect detection in metal plate.

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.

Condition Evaluation in Steel Truss Bridge with Fused Hilbert Transform,Spectral Kurtosis,and Bandpass Filter

This study is concerned with the diagnosis of discrepancies in a steel truss bridge by identifying dynamic properties from the vibration response signals of the bridges.The vibration response signals collected at bridges under three different vehicular speeds of 10 km/hr,20 km/hr,and 30 km/hr are analyzed using statistical features such as kurtosis,magnitude of peak-to-peak,root mean square,crest factor as well as impulse factor in time domain,and Stockwell transform in the time-frequency domain.The considered statistical features except for kurtosis show uncertain behavior.The Stockwell transform showed low-resolution outcomes when the presence of noise in the recorded vibration responses.The elimination of noise and extraction of meaningful dynamic properties from the vibration responses is done by applying a new method which comes from the fusion of Hilbert transform with Spectral kurtosis and bandpass filtering.The outcomes obtained from Hilbert transform processed residual signals which are further filtered using bandpass filter show more robustness and accuracy in characterizing bridge modal frequencies from the noisy vibration responses.The proposed method produces a high-resolution frequency response which can unveil the joint discrepancy in the bridge structure.

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.

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.

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.

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.

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.