Characteristics analyzing and parametric modeling of the arc sound in CO_2 GMAW for on-line quality monitoring

For on-line monitoring of welding quality, the characteristics of the arc sound signals in short circuit CO2 GMAW were analyzed in the time and frequency domains. The arc sound presents a series of ringing-like oscillations that occur at the end of short circuit i. e. the moment of arc re-ignition, and distributes mainly in the frequency band below 10 kHz. A concept of the arc tone channel and its equivalent electrical model were suggested, which is considered a time-dependent distributed parametric system of which the transmission properties depend upon the geometric and physical characteristics of the arc and surroundings, and is excited by the sound source results from the change of arc energy so that results in arc sound. The linear prediction coding （ LPC ） model is an estimation of the tone channel. The radial basis function （ RBF ） neural networks were built for on-line pattern recognition of the gas-lack in welding, in which the input vectors were formed with the LPC coefficients. The test results proved that the LPC model of arc sound and the RBF networks are feasible in on-line quality monitoring.

Feature Extraction and Dimensionality Reduction of Arc Sound under Typical Penetration Status in Metal Inert Gas Welding

Arc sound is well known as the potential and available resource for monitoring and controlling of the weld penetration status,which is very important to the welding process quality control,so any attentions have been paid to the relationships between the arc sound and welding parameters.Some non-linear mapping models correlating the arc sound to welding parameters have been established with the help of neural networks.However,the research of utilizing arc sound to monitor and diagnose welding process is still in its infancy.A self-made real-time sensing system is applied to make a study of arc sound under typical penetration status,including partial penetration,unstable penetration,full penetration and excessive penetration,in metal inert-gas（MIG） flat tailored welding with spray transfer.Arc sound is pretreated by using wavelet de-noising and short-time windowing technologies,and its characteristics,characterizing weld penetration status,of time-domain,frequency-domain,cepstrum-domain and geometric-domain are extracted.Subsequently,high-dimensional eigenvector is constructed and feature-level parameters are successfully fused utilizing the concept of primary principal component analysis（PCA）.Ultimately,60-demensional eigenvector is replaced by the synthesis of 8-demensional vector,which achieves compression for feature space and provides technical supports for pattern classification of typical penetration status with the help of arc sound in MIG welding in the future.

Weld Seam Deviation Prediction of Gas Metal Arc Welding Based on Arc Sound Signal

Weld seam deviation prediction is the key to weld seam tracking control, which is of great significance for realizing welding automation and ensuring welding quality. Aiming at the problem of weld seam deviation prediction in GMAW (gas metal arc welding), a method of weld seam deviation prediction based on arc sound signal is proposed. By analyzing the feature of the arc sound signal waveform, the time domain feature of the arc sound signal is extracted. The wavelet packet analysis method is used to analyze the time-fre- quency domain feature of the arc sound signal, and the wavelet packet energy feature is extracted. The time domain feature and wavelet packet energy feature are used to establish the feature vector, and the BP (back propagation) neural network is used to realize the weld seam deviation prediction. The results show that the method proposed in this paper has a good weld seam deviation prediction effect, with a mean absolute error of 0.234 mm, which provides a new method for GMAW weld seam recognition.

Relationship between sound signal and weld pool status in plasma arc welding

The sound features of the weld pool status in plasma arc welding were systematically investigated after the sound signal was collected with a microphone. The results show that it is difficult to extract information about the weld pool status directly in time domain although the sound signal varies with the weld pool behaviors to some extent. The frequency spectra of the sound signal contain plenty of information about the weld pool behaviors. It is shown from the analysis of the sound generation mechanism that the sound signal of plasma arc welding is mainly caused by the weld pool oscillation, the power source fluctuation and so on. R S algorithm is designed to determine the weld pool status, and it is able to offer the feedback information for the closed loop control of the penetration quality of plasma arc welding. [

Micro arc oxidation and electrophoretic deposition effect on damping and sound transmission characteristics of AZ31B magnesium Alloy

Micro arc oxidation(MAO) and electrophoretic deposition(EPD) process are employed to fabricate a dense coating on magnesium alloy to protect it from corrosion in engineering application. The EPD film changes the damping characteristic of magnesium alloy, and both the MAO and EPD process change the bending stiffness of samples being treated. Damping loss factor(DLF) test and sound transmission experiments were carried out for AZ31 B magnesium alloy with coating fabricated by MAO and EPD processes. The results indicate that DLF is improved in frequency range from 0-850 Hz. Bending stiffness of the samples is improved with MAO and EPD treatment. As a result, the sound transmission loss(LST) is improved in the stiffness control stage of the sound transmission verse frequency curve. To the samples by electrophoresis process, the LST is improved in frequency range from 2500-3200 Hz, because the damping loss factor is improved with EPD process. The results are useful for the surface treatment to enhance the damping loss factor, LST and widespread application of magnesium alloy while improving the corrosion resistance.

基于电弧声信号的窄间隙脉冲熔化极气体保护焊侧壁熔合状态在线识别

为在焊接过程中实时了解焊缝内部的焊接状况,构建了电弧声信号实时采集系统。在焊枪摆动中心处于不同位置的情况下,进行了电弧声信号特征与侧壁熔合状态的相关性分析。分别从时域与频域中提取了与侧壁熔合状态相关性较强的电弧声特征。为进一步提高熔合状态预测的有效性,采用电弧声特征参量构建了支持向量回归的侧壁熔合状态识别模型。为减小不良特征对识别模型的影响,显著提高模型的识别精度,采用遗传算法进行了参数寻优。参数寻优后模型的总体识别率达93.33%,实现了窄间隙侧壁熔合状态的有效识别。

焊接电弧声测试技术研究

电弧声是焊接过程中产生的非稳定随机信号,与电弧行为、电弧稳定性及焊缝质量等有着密切的相关性,是研究焊接过程稳定性及焊接质量监控的重要信息源之一。文中运用虚拟仪器软件LabVIEW对电弧声信号进行采样处理,设计了采集模块对平板焊接时不设焊接参数的原始信号波形进行分析;设计了分析处理模块对电弧声信号进行特征提取;设计了分析、处理模块对信号的时域和频域特性进行分析;设计了GUI界面实现以上操作过程的交互。经实际运用,该系统对于研究焊接过程稳定性有一定的参考价值。

中厚板V型坡口GMAW电弧声信号焊缝偏差识别方法研究

针对中厚板V型坡口GMAW(熔化极气体保护焊)焊缝偏差识别问题,提出一种新型基于电弧声信号的中厚板GMAW摆动焊焊缝偏差识别方法。在V型坡口摆动焊接中,发现当摆动中心与焊缝中心出现偏差,电弧声信号呈现明显非对称性。为此,针对电弧声信号的时域和频域特征开展了进一步研究,明确了与焊缝偏差信号存在密切关联的电弧声摆动极限位置能量差、标准差、小波包第7频带和第8频带能量等特征参量。构建基于上述4类参量的GS-SVR非线性回归方程,通过电弧声特征信号检测,可实现焊接过程中焊接偏差信息的在线识别,通过左右偏差试验表明该模型具有良好精度,可满足工程实际生产需要。

某车型启动异响的分析及改善优化

随着汽车行业不断的创新发展,新技术不断的应用,乘用车驾乘感受及行驶品质都有了质的提升,消费者对乘用车的驾乘舒适性及整体NVH性能要求不断提高。传统乘用车起动机受制于结构及功率限制,往往会出现启动时异响、拖尾音、振动等问题,影响乘用车的NVH感受。针对某新车型启动异响问题,对其产生机理及影响因素进行分析,通过对起动机硬件的结构优化,标定数据的更新,结合起动过程中的NVH噪声测试、主观试驾评审,研究了起动机齿形参数、电磁开关弹簧力的优化调整对起动噪声的改善效果。最终通过优化电磁开关弹簧力、齿轮侧隙及碳刷圆弧结构解决了起动机起动异响问题。

多物理场耦合仿真下声波对直流电弧的影响

断路器等开关设备是当前直流输配电领域的研究重点与难点,开关电弧是影响断路器等设备性能的重要因素。基于磁流体动力学(MHD)理论,建立了声场影响下的直流电弧仿真模型。通过多物理场的耦合仿真,研究了在声场影响下各物理场与直流电弧的特性及相关性,通过实验对仿真结果进行检验。

MIG焊电弧声信号与熔透状态相关性

电弧声作为焊缝熔透状态监控的潜在源信号,对焊接过程质量控制和自动化生产的实现具有重要意义。以MIG焊平板对接射流过渡过程中产生的非平稳电弧声信号为研究对象,采用小波变换技术取得了良好的信噪分离效果,达到有效抑制噪声目的。在此基础上,通过合理地选择样本数据,从时域、频域和时频联合3个角度研究分析电弧声信号与焊缝熔透状态之间的内在相关性,发现电弧声信号能量及其频谱特性与熔透状态高度相关,其中1500～4500Hz频率段信号能量的变化可准确地检测出焊接过程熔透状态的改变。结果表明利用电弧声信号进行熔透状态监控是可行的,为MIG焊接过程质量监控提供了一种廉价而有效的新途径。

基于多信息融合的故障电弧保护系统的应用研究

在以故障电弧弧声、弧光、短路电流为表征信息的基础上,提出了一种新的电弧保护方案。以发生燃弧之前的弧声为判据,建立了故障电弧预警机制;以燃弧伴生信号弧光、短路电流为判据,建立了故障电弧的报警机制。实验表明,该系统可以对开关柜的故障电弧起到双重保护作用。

电弧声信号与铝合金MIG焊缝塌陷的相关性

针对铝合金MIG焊过程热积累作用强容易产生焊缝突然塌陷从而导致焊接过程失败的问题,建立电弧声信号传感系统并以焊接电弧声为研究对象,运用小波分析方法研究焊接电弧声信号与焊缝塌陷的相关性。研究表明电弧声信号的能量变化与焊缝塌陷有着明显的对应关系,电弧声总能量随着焊缝开始塌陷而增加,对电弧声进行小波分解发现不同频带有着不同的变化规律。研究结果为铝合金MIG焊实时焊接塌陷缺陷的检测提供一种新型有效的方法。

Duffing振子信号探测在故障电弧短路保护中的应用

Duffing振子信号检测技术利用混沌系统的分岔特性来检测外界信号,将待测信号作为Duffing方程周期策动力的摄动,利用初值敏感性可以获得很高的测量灵敏度和良好的抗噪性能。基于Duffing振子信号检测技术提出一种新型的故障电弧监测方法。在电弧没有发生燃弧之前有比较微弱的弧声信号,完全湮没在强背景噪声中,将含有噪声的弧声信号作为周期策动力加入混沌检测系统中,如果混沌检测系统处于间歇混沌状态,证明有电弧弧声产生,则故障电弧监测系统发出预警信号。实验表明,发生燃弧的瞬间,弧光、短路电流、弧声的幅值都有明显的跃变,以这3个电弧表征信息为判据,设定合适的幅值阈值,建立报警系统。实验结果表明该监测系统能实现对故障电弧的预警和报警功能。

MIG焊熔透电弧声小波包频带能量特征提取

焊缝熔透状态的监控对焊接过程质量控制和自动化生产的实现具有重要意义,为此建立了M IG焊熔透电弧声试验系统.通过分析射滴与射流过渡电弧声频谱特征,设计了一套针对电弧声频带能量特征提取的流程.首先对焊接过程拾取的电弧声进行小波降噪;其次采用小波包移频算法进行电弧声频带提取,消除了经典小波包迭代算法由于小波包分解过程中的隔点采样而产生的频率混叠现象;然后计算频带能量并构造其特征向量.结果表明,所提取的频带能量特征能较好的反映焊缝熔透状态,为后续基于电弧声的熔透诊断奠定基础.

开关柜内部故障电弧探测法的研究现状及趋势

鉴于故障电弧是电力系统开关柜内部的灾难性故障之一,综合分析了国内外对故障电弧探测方法的研究及应用现状,在对现有各种故障电弧探测方法进行介绍和分析,指出各种探测方法的技术特点、局限性和适用范围的基础上,提出了故障电弧的早期预测预警探测方法及故障电弧探测算法智能化的发展趋势。通过研究和提取故障电弧放电前伴随的早期弧声、弧光的特征信息,实时在线监测该特征弧声、弧光是否存在即可断定故障电弧是否将会发生,从而改变了目前故障电弧的事后被动检测。可实现基于特征弧声、弧光的早期预测预警防护机制,把故障消除在发生早期,降低和避免了故障电弧产生时对开关设备造成的损失。

CO_2焊接过程动态信号的特征分析

利用小波分析方法 ,对所采集的CO2 焊接电流、电弧电压和电弧声波进行了信号降噪处理和奇异点分析 ,提取不同频率范围的声波能量作为表征焊接过程状态变化的特征向量。采用统计学方法研究了特征值集合与焊接飞溅的相关性 ,完成了特征集合的评价与降维 。

基于小波分析的故障电弧伴生弧声特征提取

在故障电弧伴生早期弧声频谱特性研究的基础上,提出基于小波包分解的早期弧声频带局部能量特征参数的快速提取方法。早期弧声的功率谱分析表明,故障电弧发生之前,在可听波段产生5 kH z^10 kH z的电弧声,其带宽和中心频率与电极形状、放电距离、放电电压等试验条件有关。利用小波包多分辨率技术对弧声信号进行三层分解,对各子频带进行能量统计,根据不同子频带能量的分布特征建立起"能量—信号"的映射关系。实验研究表明,弧声出现前后第二子频带S31和第三子频带S32能量变化明显,可以作为识别早期弧声的特征参数。通过在线监测信号这两个特征子频带能量的变化即可判断早期故障弧声是否存在,从而实现故障电弧的早期预测预警。

Lyapunov指数法在故障电弧早期探测中的应用

故障电弧产生的伴生信号主要有弧光、短路电流、弧声。实验研究表明,弧前声音信号能作为故障电弧的早期表征信息,对弧前声音混沌时间序列进行相位空间重构,基于模型LE法确定混沌检测系统内置信号阈值,基于Lyapunov指数的故障电弧弧前声音信号混沌检测方法,能定量地识别湮没在复杂背景噪声中弧前声音信号。若最大Lyapunov指数大于零,表明由弧前声音信息产生,以弧前声音信息为判据提出弧前预警算法。

管道MIG焊双通道电弧声信号采集与特征分析

对管道MIG焊双通道信号采集和特征分析做了初步研究.因管道其内部是一个相对狭小密闭的空间,回声的干扰相对较大,经过对采集的双通道电弧声信号进行ICA信号分离,对分离后的信号做小波降噪处理,信噪比得到明显提高.对降噪后的信号从时域、频域和时频域角度,综合分析管内和管外电弧声信号与焊缝熔透状态之间的内在相关性,发现电弧声信号能量及其频谱特性与熔透状态高度相关,焊接过程中信号能量幅值随熔透增大而增大,为后续利用电弧声信号来控制焊接质量提供了基础.