基于改进EEMD-MB1DCNN的船用柴油机缸套-活塞环故障诊断

针对船用中高速柴油机缸套-活塞环振动信号非线性非平稳性以及同类型不同损伤程度故障发生时振动信号时频域特征相似、故障难以识别等问题,利用振动信号辨识故障,提出一种基于改进集成经验模态分解方法和多模块一维卷积神经网络端到端缸套-活塞环故障诊断方法,通过设计固有模态分量IMF信息质量筛选准则对EEMD分解出的IMFs进行重新排序,获得包含更多凸显故障特征成分的重构信号,输入到上述神经网络模型,通过振动信号分析并与现有方法比较,评估所设计IMF信息质量筛选准则与所搭建模型的性能,试验结果显示该方法能准确、有效地识别缸套-活塞环故障类型。在判断该易损件同类型不同磨损程度故障诊断中有较高的准确率,能对故障状况进行有效的特征提取与故障分类。

Signal de-noising method based on wavelet decomposition

A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavelet analysis is introduced for signal de-noising during the dynamic testing. Secondly, the theoretical basis of wavelet analysis, the choice of wavelet base and the determination of decomposed series and threshold are analyzed. Finally, the de-noising experiment for infrared detector signal is carried out on the Matlab platform. The results indicate the proposed wavelet de-noising method is effective to remove fixed frequency and high-frequency noise; furthermore, good synchronization is achieved between the de-noised signal and the useful signal components in the original signal, which is of great significance to thermocouple modeling analys- is.

基于小波变换优化EEMD结合SG的红外光谱降噪算法

红外光谱气体分析技术由于具有检测参数多、检测效率高、分析准确等优势,已经逐渐成为气测录井的主要分析手段。但是由于地层流体中的烃类气体种类多、浓度范围跨度大等因素,致使测量的光谱数据复杂,所以光谱数据的预处理尤为重要,这直接关系到测量结果的准确性,而噪声是一个极为重要的干扰因素,如何对得到的烃类光谱数据进行去噪处理是一个至关重要的问题。基于此,本文提出了小波变换优化集合经验模态分解(EEMD)结合Savitzky-Golay滤波(S-G)的红外光谱降噪算法,该算法首先利用EEMD对信号进行分解得到多个IMF分量,再利用小波变换对各IMF分量进行小波阈值去噪,最后对去噪后的各IMF分量进行重构并进行S-G滤波。实验结果表明,本文提出的算法能够同时有效的去除吸收光谱数据中高斯白噪声和脉冲噪声,还提高了吸收光谱的平滑度指标,提升了录井气体检测的准确性。

结合EEMD的噪声对消方法在遥测振动信号降噪中的应用

针对传统降噪方法难以兼顾飞行器遥测振动信号中细节信息损失和降噪性能之间的矛盾,提出一种集合经验模态分解(ensemble empirical mode decomposition,EEMD)和噪声对消相结合的降噪方法。信号经EEMD处理得到本征模态函数(intrinsic mode function,IMF),将第1阶IMF分量和其余IMF分量的累加和分别作为参考噪声和待降噪信号;利用核方法将信号映射到高维特征空间,利用映射到高维空间中的参考噪声和待降噪信号进行噪声对消。计算机仿真结果表明:该方法在避免信号细节信息损失的前提下具有良好的降噪性能,某次飞行器试验中实测数据处理结果证明方法有效和实用。

基于EEMD分解法对北武当观测站形变资料受气压干扰特征分析

本文通过选取北武当观测站的水平摆、伸缩仪,对观测数据受气压干扰明显的典型事件进行分析,研究气压变化如何对观测数据变化产生的影响,并对原始观测数据采用EEMD分解出含有干扰信息的IMF分量;对IMF分量进行Hilbert变换得出Hilbert时频图。结果表明,水平摆和伸缩仪受气压干扰明显,且气压干扰具有延时性。总之,通过EEMD方法对北武当观测站测向资料的分析,说明这种方法对气压干扰的研究有较好的效果,提高了识别干扰信息的能力。

基于EEMD-WPT的温室环境数据优化处理研究

【目的】解决温室系统中的数据采集传感器容易受到多种环境因素的干扰,从而导致数据中存在噪声的问题。【方法】提出一种集合经验模态分解(Ensemble empirical mode decomposition,EEMD)与小波包自适应阈值(Wavelet packet adaptive threshold,WPT)算法联合的数据降噪处理方法,并采用卡尔曼滤波与自适应加权平均算法对降噪后的数据进行融合。【结果】将EEMD-WPT算法应用于含噪温、湿度数据的降噪处理,相较于降噪前的数据,信噪比提升了73.08%。该算法相较于传统WPT算法具有更好的降噪效果,处理后的数据信噪比提升了40.31%,均方根误差降低了84.75%。【结论】该算法能解决数据跳动、冗余和丢失等问题,并为温室控制系统提供了有效的参数,具有较大的实际应用价值。

基于EEMD-SVM-ELM模型的月降水量预测研究

针对地表降水量数据的非线性、非平稳特征,首先利用EEMD对月降水量初始数据进行分解,再利用Lempel-Ziv复杂度算法将分量划分为高频及低频分量,使用粒子群算法(PSO)优化基学习器参数,最终构建EEMD-SVR-ELM月降水量预测模型,并采用该模型对长江下游部分城市的月降水量实际数据进行预测。结果表明,该模型的综合性能最优,具有更高的精确度。相较于单一模型,在M_(MAE)、R_(RMSE)、M_(MAPE)指标上分别降低了37.4%、41.4%、42.5%,DM检验表明该模型显著优于其他模型,说明该模型可作为月降水量预测的一种有效新方法。

融合EEMD-CNN的水电机组磨碰故障声纹识别模型

水电机组声纹信号包含大量反映内部机械状态的有效信息,为了准确提取水电机组磨碰故障声纹特征,提出一种基于聚合经验模态分解(EEMD)与卷积神经网络(CNN)相结合的水电机组磨碰声纹识别模型。首先将水电机组噪声信号进行EEMD分解,得到若干本征模态分量(IMF)和残余分量(Res),然后将得到的IMF和Res与原噪声信号构建融合特征向量;以融合特征向量为输入,碰磨故障输出,正常和碰磨故障试验数据为样本,训练CNN深度学习神经网络,得到水电机组磨碰故障识别器,识别水电机组磨碰故障。结合水机电耦合平台和实际机组试验磨碰数据,验证了所提方法对水电机组碰磨故障识别效果,平均准确率达到99.8%,且该方法识别效果显著优于其他几种识别模型。

Applications of Wavelet Analysis in Differential Propagation Phase Shift Data De-noising

Using numerical simulation data of the forward differential propagation shift （ΦDP） of polarimetric radar,the principle and performing steps of noise reduction by wavelet analysis are introduced in detail.Profiting from the multiscale analysis,various types of noises can be identified according to their characteristics in different scales,and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied,a default threshold strategy through which the threshold value is determined by the noise intensity,or a ΦDP penalty threshold strategy through which a special value is designed for ΦDP de-noising.Then,a hard-or soft-threshold function,depending on the de-noising purpose,is selected to reconstruct the signal.Combining the three noise suppression strategies and the two signal reconstruction functions,and without loss of generality,two schemes are presented to verify the de-noising effect by dbN wavelets：（1） the penalty threshold strategy with the soft threshold function scheme （PSS）; （2） the ΦDP penalty threshold strategy with the soft threshold function scheme （PPSS）.Furthermore,the wavelet de-noising is compared with the mean,median,Kalman,and finite impulse response （FIR） methods with simulation data and two actual cases.The results suggest that both of the two schemes perform well,especially when ΦDP data are simultaneously polluted by various scales and types of noises.A slight difference is that the PSS method can retain more detail,and the PPSS can smooth the signal more successfully.

Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.

Geotechnical engineering blasting:a new modal aliasing cancellation methodology of vibration signal de-noising

In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is proposed,with the goal of addressing poor filtering de-noising effects caused by the occurrences of modal aliasing phenomena in EMD blasting vibration signal decomposition processes.Test results showed that frequency of intrinsic mode function(IMF)components decomposed by PEMD gradually decreases and that the main frequency is unique,which eliminates the phenomenon of modal aliasing.In the simulation experiment,the signal-to-noise(SNR)and root mean square errors(RMSE)ratio of the signal de-noised by PEMD are the largest when compared to EMD and ensemble empirical mode decomposition(EEMD).The main frequency of the de-noising signal through PEMD is 75 Hz,which is closest to the frequency of the noiseless simulation signal.In geotechnical engineering blasting experiments,compared to EMD and EEMD,the signal de-noised by PEMD has the lowest level of distortion,and the frequency band is distributed in a range of 0-64 Hz,which is closest to the frequency band of the blasting vibration signal.In addition,the proportion of noise energy was the lowest,at 1.8%.

Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

Moving horizon based wavelet de-noising method of dual-observed geomagnetic signal for nonlinear high spin projectile roll positioning

Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.

Application of RLS adaptive filteringin signal de-noising

In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.

基于EEMD分解的阶次跟踪方法研究

摆线针轮减速器组成零部件繁多、构成复杂,工作时噪声干扰大且多在变转速、往复的复杂工况下工作,因此,难以准确提取其内部的故障特征。针对这一问题,提出了一种基于集合经验模态分解(EEMD)与阶次跟踪分析的方法,对摆线针轮减速器进行了故障诊断。首先,对采集到的时域振动信号和转速信号进行了等角度域差值采样,得到了振动信号的等角域平稳信号;然后,对等角域信号进行了集合经验模态分解,得到了若干个固有模态分量(IMFs),计算了各个固有模态分量的峭度值,选取目标模态分量进行了信号重构;接着,采用快速傅里叶变换得到了故障信号的阶次图;最后,根据减速器的传动方式、各零部件的模数,计算出了各主要部件的故障阶次,对比减速器在故障前后阶次图的能量峰值进行了故障诊断。研究结果表明:该方法能够准确提取包含故障信息的固有模态分量,实现从等时域信号到等角域信号的转换,并提取摆线针轮减速器的滚针故障阶次(8.37阶),故障准确率达到99.6%,可实现摆线针轮减速器在非平稳工况下的故障特征识别,并验证该方法的可行性和有效性。

EEMD-小波在高边坡变形信息提取中的应用研究

针对高边坡变形呈现非平稳性及数据“噪声”多源的问题,提出了一种定向滤波的变形信息提取方法。首先,利用集合经验模态分解方法分解变形时序数据,结合定量分析法判别模态分量信号频段;然后,对高频模态分量中的“噪声”利用小波函数进行“靶向”消噪处理,并对趋势项进行傅里叶级数拟合;最后,重构高边坡变形分析模型,实现真实变形量的提取。结果表明,对比分析各项检验指标,通过“靶向”消噪,各高频模态分量消噪效果明显,重构后的集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)-小波高边坡变形分析模型较原始形变和其他模型在精度指标方面提升显著,该方法可用于高边坡的变形预测分析和真实变形量提取。

Technology of signal de-noising and singularity elimination based on wavelet transform

Based on wavelet transform theory,a method for signal de-noising and singularity detection and elimination is proposed,which can reduce the noises and express local singularity.Each singularity can also be detected and located through the local modulus maxima of wavelet transform.Simulation experiments are conducted with MATLAB software.The experimental results demonstrate that the method proposed in this paper is effective and feasible.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

Application of S-transform threshold filtering in Anhui experiment airgun sounding data de-noising

As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following characteristics: its time-frequency resolution corresponding to the signal frequency, reversible inverse transform, basic wavelet that does not have to meet the permit conditions. We combined the threshold method, proposed the S-transform threshold filtering on the basis of S transform timefrequency filtering, and processed airgun seismic records from temporary stations in "Yangtze Program"(the Anhui experiment). Compared with the results of the bandpass filtering, the S transform threshold filtering can improve the signal to noise ratio(SNR) of seismic waves and provide effective help for first arrival pickup and accurate travel time. The first arrival wave seismic phase can be traced farther continuously, and the Pm seismic phase in the subsequent zone is also highlighted.

Partial Discharge Source Classification and De-Noising in Rotating Machines Using Discrete Wavelet Transform and Directional Coupling Capacitor

This paper introduces a new method to separate PD1 from other disturbing signals present on the high voltage genera-tors and motors. The method is based on combination of a pattern classifier, the Discrete Wavelet Transform (DWT), to de-noise PD and Time-Of-Arrival method to separate PD sources. Furthermore, it will be shown that it can recognize PD sources including rotating machine’s internal and external discharge pulses (e.g. on the bus bar).