Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition

The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.

Discrete Wavelet Multi-scale Decomposition of the Temporal Gravity Variations in North China

On the basis of the absolute and relative gravity observations in North China,spatial dynamic variation of regional gravity fields is obtained. A multi-scale decomposition technique is used to separate anomalies at different depths,and give some explanation to gravity variation at different time space scales. Gravity variation trends in North China are improved. Based on this result and the analysis of wavelet power spectrum,the images of the depth of wavelet approximation and detail are obtained. The results obtained are of scientific significance for the deep understanding of potential seismic risk in North China from gravity variations in different time space scales.

Denoising of seismic data via multi-scale ridgelet transform

Noise has traditionally been suppressed or eliminated in seismic data sets by the use of Fourier filters and, to a lesser degree, nonlinear statistical filters. Although these methods are quite useful under specific conditions, they may produce undesirable effects for the low signal to noise ratio data. In this paper, a new method, multi-scale ridgelet transform, is used in the light of the theory of ridgelet transform. We employ wavelet transform to do sub-band decomposition for the signals and then use non-linear thresholding in ridgelet domain for every block. In other words, it is based on the idea of partition, at sufficiently fine scale, a curving singularity looks straight, and so ridgelet transform can work well in such cases. Applications on both synthetic data and actual seismic data from Sichuan basin, South China, show that the new method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data than other methods, the quality and consecutiveness of seismic event are improved obviously as well as the quality of section is improved.

Multi-scale prediction of MEMS gyroscope random drift based on EMD-SVR

To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.

Three-Dimensional Density Distribution and Seismic Activity along the Guxiang–Tongmai Segment of the Jiali Fault,Tibet

The Guxiang-Tongmai segment of the Jiali fault is situated northeast of the Namche Barwa Syntaxis in northeastern Tibet.It is one of the most active strike-slip faults near the syntaxis and plays a pivotal role in the examination of seismic activity within the eastern Himalayan Syntaxis.New study in the research region has yielded a 1:200000 gravity dataset covering an area 1500 km^(2).Using wavelet transform multiscale decomposition,scratch analysis techniques,and 3D gravity inversion methods,gravity anomalies,fault distributions,and density structures were determined across various scales.Through the integration of our new gravity data with other geophysical and geological information,our findings demonstrate substantial variations in the overall crustal density within the region,with the fault distribution closely linked to these density fluctuations.Disparities in stratigraphic density are important causes of variations in the capacity of geological formations to endure regional tectonic stress.Earthquakes are predominantly concentrated within the density transition zone and are primarily situated in regions of elevated density.The hanging wall stress within the Guxiang-Tongmai segment of the Jiali fault exhibits a notable concentration,marked by pronounced anisotropy,and is positioned within the density differential zone,which is prone to earthquakes.

一种新的QR-SIC-MSD联合MIMO信号检测算法

针对MIMO信号检测中球形译码(Sphere decoding,SD)算法在低信噪比时接近最大似然(Maximum likelihood,ML)算法复杂度的缺点,提出了一种基于QR分解的串行干扰抵消(Successive interference cancellation,SIC)和修改的球形译码(Modified sphere decoding,MSD)联合MIMO信号检测算法,称之为QR-SIC-MSD算法。该算法在低信噪比时采用SIC算法,在高信噪比时,采用MSD算法,根据噪声方差仅选择一次初始搜索半径,若搜索失败则用SIC解代替。通过SIC算法与MSD算法的结合,大大地降低了球形译码算法的复杂性,同时保证了在中高信噪比时逼近ML算法性能,在低信噪比时接近ML算法性能。文中同时给出了完整的算法流程图及计算机仿真结果。

Interpretations of gravity and Songliao Basin with Wavelet magnetic anomalies in the Multi-scale Decomposition

In this paper, we introduce the method of Wavelet Multi-scale Decomposition （WMD） combined with Power Spectrum Analysis （PSA） for the separation of regional gravity and magnetic anomalies. The Songliao Basin is situated between the Siberian Plate and the North China Plate, and its main structural trend of gravity and magnetic anomaly fields is NNE. The study area shows a significant feature of deep collage-type construction. According to the feature of gravity field, the region was divided into five sub-regions. The gravity and magnetic fields of the Songliao Basin were separated using WMD with a 4th order separation. The apparent depth of anomalies in each order was determined by Logarithmic PSA. Then, the shallow high-frequency anomalies were removed and the 2nd-4th order wavelet detail anomalies were used to study the basin＇s major faults. Twenty-six faults within the basement were recognized. The 4th order wavelet approximate anomalies were used for the inversion of the Moho discontinuity and the Curie isothermal surface.

A Multi-Scale Method for PM2.5 Forecasting with Multi-Source Big Data

In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.

Flow characterization of high-pressure dense-phase pneumatic conveying of coal powder using multi-scale signal analysis

Flow characterization of high-pressure dense-phase pneumatic conveying of coal powder is not fully understood. To further reveal the dynamic behavior of coal particles in dense-phase pneumatic conveying pipelines, a method for the scale decomposition of particle motion based on empirical mode decompo- sition and Hurst analysis of experimental electrostatic signals is reported. This allows the multi-scale motion characteristics of single coal particles and particle clusters to be determined. Micro-, meso-, and macro-scale subsets were reconstructed, which reflected the different behaviors of the coal particles： specifically, dynamic features of the micro-scale subset represented features of single particle collisions and frictional interactions; dual ffactal characteristics of the meso-scale subset described the motion of coal particle clusters; and features of the macro-scale subset reflected persistent dynamic behavior of the entire pneumatic conveying system. Motion behavior of single particles and particle clusters could be respectively investigated by considering the relative energies of the micro- and meso-scale contributions to the electrostatic signal. This was verified both by theoretical analysis and experiment.

Crustal density structure of the southern segment of the Liaocheng-Lankao fault, China

The 1:200,000 middle-large scale Bouguer gravity anomaly data covering the southern segment of the Liaocheng-Lankao fault(SLLF)and its vicinity are analyzed with two methods.First,the Bouguer gravity anomaly data are decomposed by two-dimensional(2 D)wavelet to make the family of multi-scale modes correspond with density structure at different depths.Second,a two and half dimension(2.5 D)human-computer interaction inversion of the Bouguer gravity anomaly data are conducted with the constraints provided by two deep seismic sounding profiles(DSS1 and DSS2)crossing the study area to get the crustal density profiles.Based on the integrated study,we can draw the following conclusions:1)SLLF appears to be a deep fault with almost vertical dipping and rooted into the uppermost mantle;2)In the middle to upper crust SLLF shows an clear turning patterns and segmentation features;3)In the study area the epicentral distributions of the precisely re-located small earthquakes and the historical large earthquakes have a good correspondence with the turning patterns and segmentation features of SLLF;and 4)The results of the horizontal slices from 2 D wavelet decomposition show that there are significant differences in the density structure on the two sides of the fault.A well-defined concave structure with low density exists in the upper crust of the Dongming Depression on the west side of the fault,with the concave center being estimated at a depth of about 8 km.In contrast,the upper crust on the east side presents a relative thinner pattern in depth with a bit higher density.Meanwhile,the low-density structure in the middle crust underneath the fault is presumably caused by the uplift of the upper mantle materials and their intrusion along the deep rupture system.This paper clarified the inconsistency of fault system and epicenters of small earthquakes from upper to lower crust.The results indicated that the fault system plays an important governing role to the seismicity in this area.

Anomaly Feature Extraction of the Gravity Field before the Jiuzhaigou Earthquake in 2017

The M_S7. 0 Jiuzhaigou earthquake occurred on August 8,2017. The earthquake occurred in the vicinity of the Tazang fault,the Minjiang fault and the Huya fault,where the focal mechanism is of the strike slip type. The static and dynamic anomalies of the gravity field can provide important physical field information for studying the structural properties of deep crust. Multi-scale decomposition techniques are used to separate Bouguer gravity at different depths and give some explanation to gravity variations at different time space scales. The results indicate that the wavelet multi-scale results of the EGM2008 model and the measured gravity data are consistent. Through comparative analysis,it is found that the Jiuzhaigou earthquake occurred in the stress enhanced region. The variation of gravity field at different time scales has a certain scientific significance for further understanding potential earthquake risk trend.

各向异性导向滤波的红外与可见光图像融合

目的针对红外与可见光图像融合时易产生边缘细节信息丢失、融合结果有光晕伪影等问题,同时为充分获取多源图像的重要特征,将各向异性导向滤波和相位一致性结合,提出一种红外与可见光图像融合算法。方法首先,采用各向异性导向滤波从源图像获得包含大尺度变化的基础图和包含小尺度细节的系列细节图;其次,利用相位一致性和高斯滤波计算显著图,进而通过对比像素显著性得到初始权重二值图,再利用各向异性导向滤波优化权重图,达到去除噪声和抑制光晕伪影;最后,通过图像重构得到融合结果。结果从主客观两个方面,将所提方法与卷积神经网络(convolutional neural network,CNN)、双树复小波变换(dual-tree complex wavelet transform,DTCWT)、导向滤波(guided filtering,GFF)和各向异性扩散(anisotropic diffusion,ADF)等4种经典红外与可见光融合方法在TNO公开数据集上进行实验对比。主观分析上,所提算法结果在边缘细节、背景保存和目标完整度等方面均优于其他4种方法;客观分析上,选取互信息(mutual information,MI)、边缘信息保持度(degree of edge information,QAB/F)、熵(entropy,EN)和基于梯度的特征互信息(gradient based feature mutual information,FMI_gradient)等4种图像质量评价指数进行综合评价。相较于其他4种方法,本文算法的各项指标均有一定幅度的提高,MI平均值较GFF提高了21.67%,QAB/F平均值较CNN提高了20.21%,EN平均值较CNN提高了5.69%,FMI_gradient平均值较GFF提高了3.14%。结论本文基于各向异性导向滤波融合算法可解决原始导向滤波存在的细节"光晕"问题,有效抑制融合结果中伪影的产生,同时具有尺度感知特性,能更好保留源图像的边缘细节信息和背景信息,提高了融合结果的准确性。

Analysis of reconstructed annual precipitation from tree-rings for the past 500 years in the middle Qilian Mountain

The ring-width chronology of a Juniperus przewalskii tree from the middle of the Qilian Mountain was constructed to estimate the annual precipitation (from previous August to current July) since AD 1480.The reconstruction showed four major alternations of drying and wetting over the past 521 years.The rainy 16th century was followed by persistent drought in the 17th century.Moreover,relatively wet conditions persisted from the 18th to the beginning of 20th century until the recurrence of a drought during the 1920s and 1930s.Based on the Empirical Mode Decomposition method,eight Intrinsic Mode Functions (IMFs) were extracted,each representing unique fluctuations of the reconstructed precipitation in the time-frequency domain.The high amplitudes of IMFs on different timescales were often consistent with the high amount of precipitation,and vice versa.The IMF of the lowest frequency indicated that the precipitation has undergone a slow increasing trend over the past 521 years.The 2-3 year and 5-8 year time-scales reflected the characteristics of inter-annual variability in precipitation relevant to regional atmospheric circulation and the El Ni?o-Southern Oscillation (ENSO),respectively.The 10-13 year scale of IMF may be associated with changing solar activity.Specifically,an amalgamation of previous and present data showed that droughts were likely to be a historically persistent feature of the Earth's climate,whereas the probability of intensified rainfall events seemed to increase during the course of the 19th and 20th centuries.These changing characteristics in precipitation indicate an unprecedented alteration of the hydrological cycle,with unknown future amplitude.Our reconstruction complements existing information on past precipitation changes in the Qilian Mountain,and provides additional low-frequency information not previously available.

Directional EMD and its application to texture segmentation

In this paper we present the definition and framework of Directional Empirical Mode Decomposition (DEMD) and use DEMD to do texture segmentation. As a new technique of time-frequency analysis, EMD decomposes signals by sifting and then analyzes the instantaneous frequency of the obtained components called Intrinsic Mode Functions (IMFs). Compared with Bidimensional EMD (BEMD) which only extracts textures by radial basis function interpolation, the virtues of DEMD include: the directional quality is considered in this framework; four features can be extracted for each point from the decomposition. The technique of selecting directions for DEMD based on texture’s Wold theory is also presented. Experimental results indicate the effectiveness of the method for texture segmentation. In addition, we show the explanation for the DEMD’s ability for texture classification from visual views.

A Continuum-Atomistic Multi-Timescale Algorithm for Micro/Nano Flows

Amulti-timescale algorithmis proposed for simulating time-dependent problems in micro-and nano-fluidics.The total simulation domain is spatially decomposed into two regions.Molecular dynamics is employed in the crucial interfacial regions and continuum hydrodynamics is adopted in the remaining bulk regions.The coupling is through“constrained dynamics”in an overlap region.Our time scheme is based on the time scale separation between the continuum macro time step and molecular micro time step.This allows the molecular dynamics during one macro time step to be treated as in quasi-steady state.Therefore,molecular simulation is only performed in two shorter time intervals.Through linear extrapolation of macroscopic velocities and re-initialization of particle configurations,we can significantly reduce the total computational cost.We demonstrate and discuss our time algorithm through hybrid simulation of channel flow driven by a sinusoidally moving top wall.Converging results are achieved for cases of large separation of time scale with much less computational cost than with the original hybrid simulation without time extrapolation.