基于BP神经网络的超声振动钻削钻头磨损状态监测试验研究

搭建了超声轴向振动钻削钻头磨损状态的钻削力和声发射信号采集系统,采集不同磨损状态下钻中区域的钻削力和声发射信号进行小波分解,得到与钻头磨损状态相关的特征量作为识别钻头磨损状态的特征参数,输入到建立的6-13-3的三层BP神经网络模型中进行融合,识别钻头磨损状态。试验结果表明,通过BP神经网络技术将钻削力和声发射信号融合识别钻头磨损的准确率约88.9%,能够有效监测钻头磨损状态。     

基于连续小波变换的钻削力信号灰度矩特征提取

利用小波分析良好的时频特性,分析了钻削过程中钻削力信号在时间-尺度域中的变化特征,提出用“灰度矩”的概念来描述连续小波变换的统计特性,并通过实验研究了整个钻头磨损历程中钻削力信号小波变换结果的“1 1”阶矩的变化规律。结果表明:随着钻头磨损的增加,其“1 1”阶矩统计特征呈上升趋势,根据其变化特征可有效实现钻头磨损状态的监测。     

基于小波包分析和LS-SVM的钻削刀具状态识别研究

为了有效地识别钻削刀具磨损状态,提出一种基于小波包分析和最小二乘支持向量机(LS-SVM)的状态识别方法。通过在线监测钻削加工过程中的钻削轴向力和刀具状态,采用时域分析、频域分析以及小波包分析法对刀具磨损状态的信号进行特征向量提取,建立基于最小二乘支持向量机(LS-SVM)的分类识别模型。通过试验验证了该方法可以提高刀具磨损状态的识别精度。     

钻削振动信号小波包分频谱减特征增强方法

针对在钻削加工噪声背景下振动信号微弱特征识别和提取困难的问题,提出了一种基于小波包分频谱减的钻削振动信号特征增强方法。首先,在经典谱减法原理的基础上,将钻削前机床空转信号视为监测信号的"加性噪声";其次,根据钻削过程振动信号的特点,采用小波包分解方法将"加性噪声"和监测信号分成多个子频带;最后,对每个子频带内"加性噪声"的相应频带进行谱减处理后,重构钻削振动信号。仿真和实验结果表明,该方法能有效降低环境噪声对钻削过程特征提取的影响,从而达到增强监测信号特征的目的,同时建立了钻削过程与监测信号之间很好的映射关系模型。     

基于双信号融合的超声振动钻削钻头磨损状态监测研究

为了监测超声振动钻削过程中钻头的磨损状态,构建了超声振动钻削钻头的振动信号和AE信号的采集系统,通过采集不同磨损状态下钻头的振动信号和AE信号,对其进行小波分解,得到与钻头磨损相关的特征值,将二者融合后作为神经网络的输入,输入至构建的12-10-3的BP神经网络中,进行钻头磨损状态的识别。试验结果表明,所建BP神经网络通过振动和AE的融合信号对钻头的有效识别率为91.7%,可以有效对钻头的磨损状态进行识别。     

钻头磨损检测与剩余寿命评估

对钻头的磨损程度进行实时检测有助于对钻削加工过程实施预防性维护,提醒及时换刀。针对自动化生产中的刀具监测问题,给出一个基于主轴电流检测的钻头磨损状态分析和剩余寿命预测的应用策略。通过主轴电流传感器采样加工过程的电流信号,使用一个滑动窗口从连续采样数据中得到真实加工段数据,采用小波包分解的方法进行特征提取。基于Fisher标准筛选出最能表达磨损过程的若干特征。最后利用逻辑回归法和自回归滑动平均模型相结合的方法评估当前钻削加工的可靠性,预测钻头的剩余寿命。试验证明此方法的有效性,可为换刀决策提供依据。     

钻削加工状态在线检测方法研究

无人值守的加工过程需要在线地自动识别机器健康状态.对钻削过程中钻头的磨损情况的检测可及时诊断出钻头过度磨损时刻,提醒及时换刀,在保证加工质量的前提下,尽可能地利用钻头的有效加工能力.鉴于电流易于获取,代价较低的特点,通过闭环电流(霍尔元件)传感器检测主轴电机的电流,并同时安装力传感器作对照,首先基于小波包分解的方法进行滤波,然后再次分解后提取出各节点的能量特征,并按照识别磨损或豁口的要求定义了两种特征选择的方法,筛选出最佳识别特征.实验结果表明,本文所述方法可有效识别出过度磨损及刃口破损时刻,提醒及时换刀,保障了加工过程的正常进行.     

基于小波包能量和调制信号双谱边带估计的齿轮磨损监测

为提高调制信号双谱边带估计的分析效果和鲁棒性,提出了小波包能量和调制信号双谱边带估计相结合的磨损特征提取方法.首先,使用小波包变换将信号分解为多个频段,计算每个频段的小波包能量,选取小波包能量较高的频段进行重构,以达到优选分析频段和降低计算量的目的;然后,对重构信号进行调制信号双谱边带估计以提取齿面磨损特征,并通过合理构建指标实现齿面磨损损伤状态的在线监测.仿真与试验结果表明,所提出方法具有更强的鲁棒性,能够更准确地监测齿面磨损的发展过程,评估磨损损伤程度.     

基于钻削力的微钻头破损特征量的提取

建立了微孔钻削监测系统，对采集的正常钻削与钻头破损两种状态下的钻削力信号进行分析和处理，提出了分别在低频段和高频段能够反映钻头破损状态的特征量。     

频带能量特征法在声发射刀具磨损监测系统中的应用

基于对声发射(AE)信号特点的分析和小波包分解理论对不平稳信号特征提取的优势,提出一种利用AE信号的能量变化来监测刀具磨损状态的方法。该方法利用db8小波基对AE信号进行5层小波包分解,将分解后各频带上的能量值作为特征参数,并组成特征向量。分别提取在新刀和刀具磨损状态下的特征向量,根据其变化即可判别刀具磨损的程度。试验结果验证了该方法在刀具磨损判析中的可用性。     

APPLICATION OF WAVELET PACKET ANALYSIS IN DRILL WEAR MONITORING

In this work, an attempt has been made to develop a drill wear monitoring system which is independent to cutting conditions of the drilling process. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. An advanced signal processing technique, the wavelet packet transform has been used on the acquired current signature to extract features for indirect representation to the amount of drill wear. Experimental sensitivity analysis reveals that in comparison to time domain features, wavelet packet features are more sensitive to flank wear and less sensitive to the cutting conditions. A multilayer neural network model has then been developed to correlate the extracted wavelet packet features with drill flank wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the flank wear with acceptable accuracy.     

APPLICATION OF WAVELET PACKET ANALYSIS IN DRILL WEAR MONITORING

In this work, an attempt has been made to develop a drill wear monitoring system which is independent to cutting conditions of the drilling process. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. An advanced signal processing technique, the wavelet packet transform has been used on the acquired current signature to extract features for indirect representation to the amount of drill wear. Experimental sensitivity analysis reveals that in comparison to time domain features, wavelet packet features are more sensitive to flank wear and less sensitive to the cutting conditions. A multilayer neural network model has then been developed to correlate the extracted wavelet packet features with drill flank wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the flank wear with acceptable accuracy.     

Tool wear monitoring in drilling using force signals

Utilization of force signals to achieve on-line drill wear monitoring is presented in this paper. A series of experiments were conducted to study the effects of tool wear as well as other cutting parameters on the cutting force signals and to establish the relationship between force signals and tool wear as well as other cutting parameters when drilling copper alloy. These experiments involve four independent variables; spindle rotational speed ranging from 600 to 2400 rev min⁻¹, feed rate ranging from 60 to 200 mm min⁻¹, drill diameter ranging from 5 to 10 mm, and average flank wear ranging from 0.1 to 0.9 mm. A statistical analysis provided good correlation between average thrust and drill flank wear. The relationship between cutting force signals and cutting parameters as well as tool wear is then established. The relationship can then be used for on-line drill flank wear monitoring. Feasibility studies show that the use of force signal for on-line drill flank wear monitoring is feasible.     

基于小波包与LS—SVM的气阀故障诊断

基于小波包原理,对柴油机的缸盖振动信号进行小波包分解,利用“频带能量”的特征提取方法得到特征向量,并作为LS-SVM的输入进行训练和分类检验,提出了一种基于小波包和LS—SVM的气阀故障诊断方法。结果表明不同状态下的气阀漏气故障能得到识别和分类,且具有较高的精度。     

小波包和小波脊线相结合的解调方法在液压泵故障诊断中的应用

针对轴向柱塞泵故障振动信号呈现出的非平稳和非线性特点,提出了一种基于小波包能量法与小波脊线法相结合的信号解调方法,将其用于液压泵故障诊断中的信号解调过程。该方法首先对原始振动信号进行功率谱分析,明确故障振动信号反映出的能量集中频带带宽;根据确定的带宽和原始信号分析频率设定小波包分解的层数,采用小波包能量法提取出分解系数对应频带能量最大的特征信息进行信号重构;利用小波脊线法对重构后的频带信号进行解调处理,通过信号的包络解调谱提取故障的特征频率,利用解调后的时频谱对液压泵单柱塞滑靴磨损、斜盘磨损以及中心弹簧故障进行分析。通过实验结果验证,该方法能有效地对液压泵的故障信号进行解调,并能找出反映故障的敏感特征频率。     

基于小波多分辨率分析和RBF神经网络的刀具磨损状态识别

刀具磨损监测对于提高加工过程的精度和自动化程度具有重要意义。本文提出一种基于RBF函数神经网络的刀具磨损状态监测模式。该系统利用声发射传感器对切削过程进行监测,采用多分辨率小波分解技术从声发射信号中提取反映刀具磨损的特征向量,并输入RBF神经网络,实现了刀具磨损的自动识别。     

钻头磨损状态可拓物元评价方法研究

基于可拓物元模型的分析,提出了钻头磨损状态可拓评价方法。通过研究,首先设计了检测钻削力信号、声发射信号、振动信号和机床功率信号的钻头磨损检测系统,在构造经典域物元、节域物元和待评物元的基础上,通过关联函数的推演,对钻头磨损状态进行可拓评价分析,获得了客观合理的评价结果。通过与实际磨损状态比较,证明可拓评价方法具有操作简单、可靠性高和实用性强等特点。