Features of energy distribution for blast vibration signals based on wavelet packet decomposition

Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Based on the characteristics of short-time nonstationary random signal, the laws of energy distribution are investigated for blasting vibration signals in different blasting conditions by means of the wavelet packet analysis technique. The characteristics of wavelet transform and wavelet packet analysis are introduced. Then, blasting vibration signals of different blasting conditions are analysed by the wavelet packet analysis technique using MATLAB; energy distribution for different frequency bands is obtained. It is concluded that the energy distribution of blasting vibration signals varies with maximum decking charge,millisecond delay time and distances between explosion and the measuring point. The results show that the wavelet packet analysis method is an effective means for studying blasting seismic effect in its entirety, especially for constituting velocity-frequency criteria.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Improved Method and Application of EMD Endpoint Continuation Processing for Blasting Vibration Signals

In order to deal with the non-stationary characteristics of blasting vibration signals and the end issue in the empirical mode decomposition(EMD), an improved endpoint continuation method is proposed. First, the linear continuation method of extreme points is used to determine the extremum of the signal endpoint fast. Secondly, the extreme points of transition section outside the signal ends are obtained by a mirror continuation method of extreme points, and then the envelope and continuation curve of the transition section of the signal are constructed. Lastly, the sinusoid of the stationary section outside the signal is constructed to achieve the continuation curve from the transition section to the stationary section. Based on the "singular extreme points" phenomenon of blasting vibration signal, the negative maxima and positive minimum are eliminated, then the maximum and minimum are guaranteed to appear at intervals. Thus,the number of iterations is reduced and the instability of EMD decomposition is improved. The calculation formula of amplitude, cycle and initial phase are given for the transition section and stationary section outside the signal. The endpoint processing effect of the simulated signal and the measured blasting vibration signal show that the improved endpoint continuation method can suppress the signal endpoint effect well.

FEATURE EXTRACTION OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method provides a finer analysis and better time-frequencylocalization capabilities than any other analysis methods. Both details and approximations are splitinto finer components and result in better-localized frequency ranges corresponding to each node ofa wavelet packet tree. For the punpose of feature extraction, a hard threshold is given and theenergy of the coefficients above the threshold is used, as a criterion for the selection of the bestvector. The feature extraction of a vibration signal is accomplished by computing thereconstruction signal and its spectrum. When applied to a rolling bear vibration signal featureextraction, the proposed method can lead to be very effective.

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.

Influence of explosion parameters on wavelet packet frequency band energy distribution of blast vibration

Blast vibration analysis is one of the important foundations for studying the control technology of blast vibration damage. According to blast vibration live data that have been collected and the characteristics of short-time non-stationary random signals, the wavelet packet energy spectrum analysis for blast vibration signal has made by wavelet packet analysis technology and the signals were measured under different explosion parameters （the maximal section dose, the distance of blast source to measuring point and the section number of millisecond detonator）. The results show that more than 95% frequency band energy of the signals sl-s8 concentrates at 0-200 Hz and the main vibration frequency bands of the signals sl-s8 are 70.313-125, 46.875-93.75, 15.625-93.75, 0-62.5, 42.969-125, 15.625-82.031, 7.813-62.5 and 0-62.5 Hz. Energy distributions for different frequency bands of blast vibration signal are obtained and the characteristics of energy distributions for blast vibration signal measured under different explosion parameters are analyzed. From blast vibration signal energy, the decreasing law of blast seismic waves measured under different explosion parameters was studied and the wavelet packet analysis is an effective means for studying seismic effect induced by blast.

Identification of Grinding Wheel Wear Signature by a Wavelet Packet Decomposition Method

Grinding is known as the most complicated material removal process and the method for monitoring the grinding wheel wear has its own characteristics comparing with the approaches for detecting the wear on regular cutting tools.Research efforts were made to develop the wheel wear monitoring system due to its significance in grinding process.This paper presents a novel method for identification of grinding wheel wear signature by combination of wavelet packet decomposition(WPD) based energies.The distinctive feature of the method is that it takes advantage of the combinational information of the decomposed frequency components based on the WPD so the extracted features can be customized according to the specific monitored object to get better diagnosis effects.Experiments are researched on monitoring of grinding wheel wear states under different machining conditions.The results show that the energy ratio extracted from the measured vibration signals is consistent with the grinding wheel wear condition evaluated by experiment and the further extracted feature ratio can be used in prediction of wheel wear condition.

Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

Influence of maximum decking charge on intensity of blasting vibration

Based on the character of short-time non-stationary random signal, the relationship between the maximum decking charge and energy distribution of blasting vibration signals was investigated by means of the wavelet packet method. Firstly, the characteristics of wavelet transform and wavelet packet analysis were described. Secondly, the blasting vibration signals were analyzed by wavelet packet based on software MATLAB, and the change of energy distribution curve at different frequency bands were obtained. Finally, the law of energy distribution of blasting vibration signals changing with the maximum decking charge was analyzed. The results show that with the increase of decking charge, the ratio of the energy of high frequency to total energy decreases, the dominant frequency hands of blasting vibration signals tend towards low frequency and hlasting vibration does not depend on the maximum decking charge.

Analysis of blasting vibration signal of high steep anti-dip layered rock slope

Blasting is one of the most economical and efficient mining methods in open-pit mine production.However,behind the huge benefits,it poses a hidden threat to the quality of slope rock mass,stability of slope,and safety of nearby buildings.In order to explore the influence of blasting vibration on the stability of anti-dip layered rock slopes,herein,the site near the large-scale toppling failure area of Changshanhao gold mine stope of Inner Mongolia Taiping Mining Co.,Ltd.was selected for on-site blasting test and monitoring.The Peak Particle Velocity(PPV)measured at the monitoring point is located on the lower side of the maximum allowable vibration velocity curve that is prepared based on the allowable speed standard evaluation chart in the full frequency domain established by standards practiced in various countries such as German DIN4150,the USBM RI 8507,and Chinese GB6722-2014.This indicates that the blasting vibration has less influence on the location of the monitoring point.The vibration signals obtained in the blasting test were analyzed using the wavelet packet theory,and it was concluded that the blasting vibration signals measured in the anti-dip layered rock slope were mainly concentrated in two frequency bands of 0-80 Hz and 115-160 Hz.The sum of energy of the two frequency bands accounted for more than 99%,wherein,the energy contained in the 0-80 Hz frequency band accounted for more than 85%of the monitoring signals.The vibration signal with 0-80 Hz frequency band monitored at the slope toe was selected for the energy attenuation analysis.The results showed that the energy attenuation decreased in radial,vertical,and tangential directions.Further,the Energy Attenuation Rate per Meter(EARPM)was calculated.In conjunction with the site characteristics analysis,it was found that the energy attenuation rate was significantly affected by the rock mass characteristics of the structural plane.The slope reinforcement project can effectively reduce the absorption of vibration energy by the slope and increase slope stability.

Wavelet basis construction method based on separation blast vibration signal

As wavelet basis in wavelet analysis is neither arbitrary nor unique,the same signal dealing with different wavelet bases will generate different results.Therefore,how to construct a wavelet basis suitable for the characteristics of the analyzed signal and solve its algorithm and realization is a fundamental problem which perplexed many researchers.To solve these problems,in accordance with the basic features of the measured millisecond blast vibration signal,a new wavelet basis construction method based on the separation blast vibration signal is proposed,and the feasibility of this method is verified by comparing the practical effect of the newly constructed wavelet with other known wavelets in signal processing.

Feature-Based Vibration Monitoring of a Hydraulic Brake System Using Machine Learning

Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road.Therefore,monitoring the condition of the brake components is inevitable.The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches.The vibration signals were captured using an accelerometer sensor under a various fault condition.The acquired vibration signals were processed for extracting meaningful information as features.The condition of the brake system can be predicted using a feature based machine learning approach through the extracted features.This study focuses on a mechatronics system for data acquisitions and a signal processing technique for extracting features such as statistical,histogram and wavelets.Comparative results have been carried out using an experimental study for finding the effectiveness of the suggested signal processing techniques for monitoring the condition of the brake system.

基于小波包分解与CEEMDAN能量熵的水电机组振动信号特征提取

针对水电机组振动信号非平稳、非线性及噪声问题,提出一种基于自适应噪声完备经验模态分解(CEEMDAN)与能量熵结合的特征提取方法,首先对采集的振动信号进行小波包降噪处理,然后对降噪后信号进行CEEMDAN分解,运用相关系数法筛选有效固有模态函数(IMF)并计算其能量熵,由此构建特征向量集,最后将其输入到海洋捕食者优化支持向量机算法(MPA-SVM)进行模式识别。基于模拟信号、实测信号验证所提特征提取方法的有效性,并与其他方法作对比。结果表明,基于小波包分解与CEEMDAN能量熵的特征提取方法能准确提取特征,有效区分机组不同状态,为工程领域提供了应用价值。

小波包和1D CNN结合的刀具磨损状态识别

为监测机床切削加工过程中刀具的非线性磨损变化,提出了一种基于小波包分解和一维卷积神经网络(1D CNN)的刀具磨损状态识别方法。采集机床主轴振动数据作为监测信号,采用经信噪比定量分析后的小波包进行预处理,然后选取小波包分解后各频带的能量特征作为1D CNN的输入,实现了对刀具磨损状态的有效识别。实验表明,该模型能够实现刀具磨损状态的准确预测,相比于BP网络、能量频谱图-Alexnet和Lstm网络模型,刀具磨损状态识别率最优,平均准确率达到98.262%。

金刚石滚轮轮廓圆度误差在线判别

金刚石滚轮形面的修形技术是制造金刚石滚轮的关键技术之一,常采用金刚石砂轮磨削法对其进行精密修形,修形后的轮廓圆度误差是考量滚轮修形合格与否的重要指标。目前的轮廓圆度检测方法是人工停机取下滚轮并放置于轮廓仪上进行,极大地增加了滚轮制作的时间和成本。为此,对在五轴加工机床上的金刚石滚轮,沿其轮廓面横向磨削修形时产生的振动信号,提出基于小波包系数和随机森林的在线检测方法并对其轮廓修形状态进行识别,在修形进行状态时的识别准确率为93.3%,具有实际应用价值。

参数优化VMD结合改进小波包阈值的去噪方法

针对轴承信号故障特征容易被噪声淹没的问题,提出一种参数优化变分模态分解结合改进小波包阈值的去噪方法。首先,通过变分模态分解(Variational Mode Decomposition,VMD)结合改进粒子群算法(Improve Particle Swarm Optimization,IPSO)将含噪信号分解为若干本征模态分量(Intrinsic Mode Function,IMF)。以最大相关系数-相关峭度为准则,把IMF分为高值分量(High-value Intrinsic Mode Function,HIMF)和低值分量(Low-value Intrinsic Mode Function,LIMF)。再对LIMF进行改进小波包(Improved Wavelet Packet,IWP)阈值去噪。最后对重构信号进行包络解调,提取轴承故障特征频率,完成故障诊断。实验结果表明,该方法不仅能够避免“过扼杀”现象,并且可以得到信噪比更高的去噪信号。

基于CEEMDAN和小波包分解的闸门振动信号降噪研究

针对闸门监测振动信号去噪问题,提出基于CEEMDAN(经验模态分解)和小波包分解的闸门振动信号降噪算法,通过采用CEEMDAN和小波包分解方法进行信号去噪,可以有效处理水电站泄洪闸门振动信号中受到的外部干扰。CEEMDAN方法能够将信号分解成多个本征模态函数(IMF),每个IMF代表不同频率的振动成分,使得外部干扰和真实信号成分可以分离。随后,小波包分解能够将每个IMF进一步分解成不同尺度和频率的子频带,这有助于更准确地定位和分离干扰成分。对每个子频带应用阈值去噪技术,可以有效去除噪声,保留真实信号。由测试结果可知,该算法能很好地剔除闸门振动信号中的无用噪声,有效提高闸门振动信号的准确性。

连拱隧道中导洞不同起爆位置振动效应研究

为研究隧道掘进爆破炮孔不同起爆位置振动效应,在连拱隧道中导洞开挖过程中开展相关试验研究,并基于CEEMDAN-小波包对监测到的爆破振动信号进行降噪处理。研究表明:1)采用CEEMDAN-小波包法对爆破振动信号进行去噪,重构,能有效保留爆破振动信号真实信息;2)炮孔不同起爆位置质点峰值振速,反向起爆>中间起爆>正向起爆;振动频率大小范围,正向起爆>反向起爆>中间起爆,其中正向起爆的频率分布更广,且具有多个峰值,有利于能量朝高频转移;3)对重构后的爆破振动信号进行Hilbert变换,发现隧道掏槽段爆破瞬时能量关系为:反向起爆>中间起爆>正向起爆;随着雷管段别的增加,反向起爆和正向起爆波形较宽、质点峰值振速较大,中间起爆振动波形较窄、质点峰值振速较小;4)对比分析炮孔不同起爆位置破岩块度,反向起爆更为均匀,效果最佳。

机体表面振动信号影响因素关联性分析

为了研究机体表面振动信号各影响因素的影响规律,在不同转速、转矩、润滑油温度及配缸间隙下,对比分析振动信号的峰值、方差、标准差及均方根等4个时域参数以及小波包分解得到的各频段能量的变化趋势。结果表明:随着转速的升高,燃烧压力峰值出现波动,在活塞惯性力的主导作用下,振动信号时域特征参数及频段5以上频率成分的能量呈增加的趋势;燃烧压力峰值随着转矩的增加而增大,振动信号时域特征参数及各频段能量均呈增加的趋势;随着润滑油温度的升高,在润滑油阻尼及燃烧压力的双重作用下,振动信号时域特征参数幅值整体呈降低的趋势,频段12以上频率成分的能量呈增加的趋势;随着配缸间隙的增加,密封性降低导致燃烧压力峰值减小,但由于活塞二次运动加剧,导致振动信号时域特征参数及频段5以上频率成分的能量呈现增加的趋势。

基于小波包理论研究地下断层对爆破振动传播的影响

为了研究云南某金属地下矿山断层对爆破振动波传播的影响,选用含有断层区域3736北部中段区域、无断层区域3736南部中段区域布置测点。以云南某金属地下矿山真实采集爆破振动数据为研究对象,基于小波包变换、HHT算法、STFT算法开展爆破振动波衰减规律的研究。结果表明:云南某地下金属矿山振动波经过断层前瞬时能量幅值达到10×10^(-4)dB,经过断层后瞬时能量幅值达到1.5×10^(-4)dB,瞬时能量幅值衰减85%;断层在一定程度上会扰乱振动波能量分布情况,延缓振动能量迅速上升;振动波经过断层后低频能量衰减60%,高频能量衰减71%,总体能量功率衰减77%,无断层区域高频能量衰减18%,低频能量衰减17%,总体能量功率衰减15%;爆破振动波经过断层后,过滤大量高频能量,能量总体分布呈现向低频域方向发展的趋势。