基于多模型决策融合的刀具磨损状态监测系统关键技术研究
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摘要
刀具状态监测技术是先进制造技术的重要组成部分,是在现代传感器技术、计算机技术、信号处理技术和人工智能技术基础上发展起来的新兴技术。迄今为止,多个国家的研究单位已对刀具状态监测技术开展了深入的研究并取得了大量的研究成果,但是仍然存在很多亟需解决的问题。本文针对这些问题进行了一系列研究。首先,本文针对刀具监测信号中的噪声污染问题,提出了基于单参数对数基小波包阈值滤波算法的信号降噪方法。其次,研究了刀具状态监测的特征提取与特征融合技术,并提出了基于分步主元量分析(PPCA)的特征融和方法。最后,针对刀具多磨损量识别问题,本文采用了支持向量机(SVM)和三级集成人工神经网络识别模型,并在此基础上采用改进型D-S证据论进行决策级融合,进一步提高了刀具状态监测系统的性能。主要研究内容摘要如下：
(1)总结并研究了实际加工生产中常见的刀具磨损现象,在对比多种刀具磨损状态的监测方法之后,采用了通过监测刀具背刀面磨损量来衡量刀具磨损状态的监测方法。监测系统硬件平台搭建中传感器的选型,采用了三维力传感器和三维振动加速度传感器相结合的方案。实验结果表明这种多传感器数据级融合的监测方案提高刀具磨损状态识别系统的精确度。
(2)在信号预处理过程中,进行了趋势项消除、零均值化与小波降噪处理。为了克服小波滤波算法中的经典阈值滤波算法所存在的缺点,增加阈值函数的非线性性,本文在构造阈值函数过程中引入非线性分量,提出基于单参数对数基小波包阈值滤波的滤波算法。通过仿真实验和刀具监测信号滤波实验都证明了改进的小波阈值滤波算法的有效性和可靠性。
(3)在特征提取环节,本文采用了时域、频域、时频域、分形几何等多种方法进行信号特征提取。信号分形盒维数是建立在分形几何学上的算法,目前在刀具磨损状态监测技术中应用得较少,本文对该方法作了深入研究,得到刀具磨损信号的分形盒维数特征,并验证了其有效性。本文还对小波包分析方法在刀具磨损状态监测系统中的应用进行了深入研究,提出了基于统计学理论的小波子带能量特征,并通过实验证明了该类特征在刀具磨损状态监测中的有效性。最后提取的特征值有：绝对值总和、最大值、极值距离、标准差、绝对平均值、方差、平方根均值、峭度、歪度、自乘均值、频率幅值和、频率最大值、信号分形盒维数、小波子带能量、子带能量比、子带能量比率差。
(4)针对刀具磨损状态特征空间高维数引起的计算开销过大的问题,本文对主元量分析(PCA)算法进行了改进,提出了分部主元量分析(PPCA)算法,成功地实现了特征优化。实验结果表明,分部主元量分析算法有效地避免目标模式识别特征之间的干扰,使得重构特征空间更有利于神经网络的快速收敛,提高了系统识别精度。
(5)本文针对高维输入输出映射求解难的问题,提出了三级集成人工神经网络与支持向量机相融合的识别模型。由于传统神经网络在对大模式空间进行识别时网络训练无法正确收敛,本文提出了三级神经网络模型,并在刀具状态识别中成功应用。本文还建立了基于支持向量机的识别模型,并将这两类具有不同数学特性的智能监测模型进行决策融合,实现对刀具磨损状态的精确识别。本文第六章、第七章详细地讨论单模型识别的效果和综合两者之后的效果。
在多模型决策级融合过程中,针对如何评判SVM的识别精度问题,通常的做法是将样本与目标空间循环划分,进行交叉检验(Cross Validation),但该方法对于本文所涉及的实验方案却不适用,这是因为交叉检验在多模式输出少样本数据的情况下意义不大,因此本文提出了计算预测分类与目标分类标签距离均值的方法,有效的解决了该问题。
在决策级融合过程中采用改进的D-S证据论。以两种具有不同数学特性的智能识别模型ANN和SVM,分别对刀具磨损状态进行识别,并将识别结果进行融合得出最终决策。在融合过程中涉及到的如何获得模型的信度值这一关键问题,本文采用的方法是将智能识别模型对重复实验数据的输出误差均方值作为信度函数值。根据D-S证据理论对证据的要求,对ANN和SVM信度空间进行归一化处理。实验结果表明基于ANN和SVM的改进D-S证据论融合模型成功地解决了刀具全寿命周期内磨损状态的精确识别问题。
Tool condition monitoring is an important part of the advanced manufacturing technology. It is an emerging technology which has been developing with the modern sensor technology, computer technology, signal processing technology and artificial intelligence. In recent years, some research institutes have conducted thorough research on tool condition monitoring and have achieved many results. However, there are still a lot of problems needed to be solved. To solve the existing problems, a series of research works have been carried out. Firstly, this paper proposes an improved wavelet threshold noise canceling method to solve the question about the pollution of noises in tool monitoring data. Secondly, the feature extraction and optimization techniques of tool condition monitoring were investigated. The PPCA (Parts Principle Component Analysis) which is an improved PCA algorithm has been discussed. At last, to solve the big size tool wear state space recognition problem, the Support Vector Machine and3-level Integrated Neural Network were proposed. An improved D-S evidence theory method was used to fuse the results. These methods further improve the performance of the tool condition monitoring system. The primary contents of this paper are as follow.
(1) Common tool wear phenomenons during manufacturing have been discussed. By comparing many monitoring methodes, the wear condition of back surface of cutters has been used to represent the tool wear state. The hardware platform of monitoring system is composed of three-dimensional dynamometer and three-dimensional accelerometer. This multi-sensor data fusion monitoring method has been proved to be effective by experimental study.
(2) In the signal processing process, treatments of tendency elimination, zero-mean and wavelet denoising have been carried out. To overcome the shortcomings of classic threshold denoising algorithm, of wavelet filtering algorithms and to increase the nonlinearity of threshold function, an improved wavelet threshold noise reduction method called single parameter logarithm wavelet packet filtering algorithm has been proposed by introducing nonlinear component in the process of constructing the thresholding function. Experimental results have proven the reliability and effectiveness of the method.
(3) Signal features are extracted form time domain, frequency domain and the time-frequency domain. The fractal geometry signal feature has been extracted too. It is not applied widely in tool condition monitoring processes right now. Further investigation on this technique has been made and the fractal box dimension feature of the signal is obtained and proved to be effective. Research work for applying the wavelet analysis methode in the tool monitoring process was also carried out. And wavelet sub-band power features based on statistical theory were proposed. All features extracted from the tool monitoring signal are as follow:Sum of absolute value, Maximum value, Distance between extremum, Standard deviation, Mean of absolute value, Variance, RMS, Kurtosis, Skewness, Sum of amplitude of frequency, Maximum frequency, Fractal box-dimension, Wavelet sub-band power, Sub-band power rate, Distance of sub-band power rate.
(4) To avoid the curse of dimensionality and reduce the cost of computing which may be caused by the large feature space, the PPCA (Parts principle component analysis) methode is proposed by improving the PCA algorithm. Feature optimization was successfully achieved. Compared with the PCA, this method avoids the interference of features between different classes. The convergence speed is increased by using of PPCA approach and the precision of monitoring system was improved.
(5) This paper proposes a fusion model of the Support Vector Machine and3-level Integrated Neural Network for tool condtion monitoring, for solving the problem of high dimensional input/output mapping. Traditional neural networks can't converge well when identifying large pattern spaces. So a3-level Integrated Neural Network was proposed and it was successfully applied in tool condition recogniztion. Support Vector Machine recognition model was also established. This paper proposes the fusion model of SVM and ANNs that have different mathematical characteristics. The results of each model and twin-model have been compared in chapter6and chapter7.
In the process of decision fusion, the problem of geting the accuracy of SVM model must be solved. The commonly method is cross validation. But it is not applicable when treating the data in this programme, because the cross validation is nolonger meaningful for small size sample data with multi-patterns. A methode of calculating the mean distance between forecast classification and the target classification is proposed to solve the above problem.
D-S evidence theory is applied in decision-making level. Two intelligent models with different mathematical characteristics (ANNs and SVM) are applied separately to recognize tool wear states and the results are fused to make the final decision. The problem of geting the confidence function value of D-S theory is a key point. The root mean value of the output error of the intelligent recognition model is used as the confidence function value. According to the requirements of D-S theory, the confidence function space of SVM and ANN is normalized. Experimental results showed that the improved D-S evidence theory fusion model for SVM and ANNs successfully solved the tool wear recognition problem in the full life span of the cutting tool.
(1)总结并研究了实际加工生产中常见的刀具磨损现象,在对比多种刀具磨损状态的监测方法之后,采用了通过监测刀具背刀面磨损量来衡量刀具磨损状态的监测方法。监测系统硬件平台搭建中传感器的选型,采用了三维力传感器和三维振动加速度传感器相结合的方案。实验结果表明这种多传感器数据级融合的监测方案提高刀具磨损状态识别系统的精确度。
(2)在信号预处理过程中,进行了趋势项消除、零均值化与小波降噪处理。为了克服小波滤波算法中的经典阈值滤波算法所存在的缺点,增加阈值函数的非线性性,本文在构造阈值函数过程中引入非线性分量,提出基于单参数对数基小波包阈值滤波的滤波算法。通过仿真实验和刀具监测信号滤波实验都证明了改进的小波阈值滤波算法的有效性和可靠性。
(3)在特征提取环节,本文采用了时域、频域、时频域、分形几何等多种方法进行信号特征提取。信号分形盒维数是建立在分形几何学上的算法,目前在刀具磨损状态监测技术中应用得较少,本文对该方法作了深入研究,得到刀具磨损信号的分形盒维数特征,并验证了其有效性。本文还对小波包分析方法在刀具磨损状态监测系统中的应用进行了深入研究,提出了基于统计学理论的小波子带能量特征,并通过实验证明了该类特征在刀具磨损状态监测中的有效性。最后提取的特征值有：绝对值总和、最大值、极值距离、标准差、绝对平均值、方差、平方根均值、峭度、歪度、自乘均值、频率幅值和、频率最大值、信号分形盒维数、小波子带能量、子带能量比、子带能量比率差。
(4)针对刀具磨损状态特征空间高维数引起的计算开销过大的问题,本文对主元量分析(PCA)算法进行了改进,提出了分部主元量分析(PPCA)算法,成功地实现了特征优化。实验结果表明,分部主元量分析算法有效地避免目标模式识别特征之间的干扰,使得重构特征空间更有利于神经网络的快速收敛,提高了系统识别精度。
(5)本文针对高维输入输出映射求解难的问题,提出了三级集成人工神经网络与支持向量机相融合的识别模型。由于传统神经网络在对大模式空间进行识别时网络训练无法正确收敛,本文提出了三级神经网络模型,并在刀具状态识别中成功应用。本文还建立了基于支持向量机的识别模型,并将这两类具有不同数学特性的智能监测模型进行决策融合,实现对刀具磨损状态的精确识别。本文第六章、第七章详细地讨论单模型识别的效果和综合两者之后的效果。
在多模型决策级融合过程中,针对如何评判SVM的识别精度问题,通常的做法是将样本与目标空间循环划分,进行交叉检验(Cross Validation),但该方法对于本文所涉及的实验方案却不适用,这是因为交叉检验在多模式输出少样本数据的情况下意义不大,因此本文提出了计算预测分类与目标分类标签距离均值的方法,有效的解决了该问题。
在决策级融合过程中采用改进的D-S证据论。以两种具有不同数学特性的智能识别模型ANN和SVM,分别对刀具磨损状态进行识别,并将识别结果进行融合得出最终决策。在融合过程中涉及到的如何获得模型的信度值这一关键问题,本文采用的方法是将智能识别模型对重复实验数据的输出误差均方值作为信度函数值。根据D-S证据理论对证据的要求,对ANN和SVM信度空间进行归一化处理。实验结果表明基于ANN和SVM的改进D-S证据论融合模型成功地解决了刀具全寿命周期内磨损状态的精确识别问题。
Tool condition monitoring is an important part of the advanced manufacturing technology. It is an emerging technology which has been developing with the modern sensor technology, computer technology, signal processing technology and artificial intelligence. In recent years, some research institutes have conducted thorough research on tool condition monitoring and have achieved many results. However, there are still a lot of problems needed to be solved. To solve the existing problems, a series of research works have been carried out. Firstly, this paper proposes an improved wavelet threshold noise canceling method to solve the question about the pollution of noises in tool monitoring data. Secondly, the feature extraction and optimization techniques of tool condition monitoring were investigated. The PPCA (Parts Principle Component Analysis) which is an improved PCA algorithm has been discussed. At last, to solve the big size tool wear state space recognition problem, the Support Vector Machine and3-level Integrated Neural Network were proposed. An improved D-S evidence theory method was used to fuse the results. These methods further improve the performance of the tool condition monitoring system. The primary contents of this paper are as follow.
(1) Common tool wear phenomenons during manufacturing have been discussed. By comparing many monitoring methodes, the wear condition of back surface of cutters has been used to represent the tool wear state. The hardware platform of monitoring system is composed of three-dimensional dynamometer and three-dimensional accelerometer. This multi-sensor data fusion monitoring method has been proved to be effective by experimental study.
(2) In the signal processing process, treatments of tendency elimination, zero-mean and wavelet denoising have been carried out. To overcome the shortcomings of classic threshold denoising algorithm, of wavelet filtering algorithms and to increase the nonlinearity of threshold function, an improved wavelet threshold noise reduction method called single parameter logarithm wavelet packet filtering algorithm has been proposed by introducing nonlinear component in the process of constructing the thresholding function. Experimental results have proven the reliability and effectiveness of the method.
(3) Signal features are extracted form time domain, frequency domain and the time-frequency domain. The fractal geometry signal feature has been extracted too. It is not applied widely in tool condition monitoring processes right now. Further investigation on this technique has been made and the fractal box dimension feature of the signal is obtained and proved to be effective. Research work for applying the wavelet analysis methode in the tool monitoring process was also carried out. And wavelet sub-band power features based on statistical theory were proposed. All features extracted from the tool monitoring signal are as follow:Sum of absolute value, Maximum value, Distance between extremum, Standard deviation, Mean of absolute value, Variance, RMS, Kurtosis, Skewness, Sum of amplitude of frequency, Maximum frequency, Fractal box-dimension, Wavelet sub-band power, Sub-band power rate, Distance of sub-band power rate.
(4) To avoid the curse of dimensionality and reduce the cost of computing which may be caused by the large feature space, the PPCA (Parts principle component analysis) methode is proposed by improving the PCA algorithm. Feature optimization was successfully achieved. Compared with the PCA, this method avoids the interference of features between different classes. The convergence speed is increased by using of PPCA approach and the precision of monitoring system was improved.
(5) This paper proposes a fusion model of the Support Vector Machine and3-level Integrated Neural Network for tool condtion monitoring, for solving the problem of high dimensional input/output mapping. Traditional neural networks can't converge well when identifying large pattern spaces. So a3-level Integrated Neural Network was proposed and it was successfully applied in tool condition recogniztion. Support Vector Machine recognition model was also established. This paper proposes the fusion model of SVM and ANNs that have different mathematical characteristics. The results of each model and twin-model have been compared in chapter6and chapter7.
In the process of decision fusion, the problem of geting the accuracy of SVM model must be solved. The commonly method is cross validation. But it is not applicable when treating the data in this programme, because the cross validation is nolonger meaningful for small size sample data with multi-patterns. A methode of calculating the mean distance between forecast classification and the target classification is proposed to solve the above problem.
D-S evidence theory is applied in decision-making level. Two intelligent models with different mathematical characteristics (ANNs and SVM) are applied separately to recognize tool wear states and the results are fused to make the final decision. The problem of geting the confidence function value of D-S theory is a key point. The root mean value of the output error of the intelligent recognition model is used as the confidence function value. According to the requirements of D-S theory, the confidence function space of SVM and ANN is normalized. Experimental results showed that the improved D-S evidence theory fusion model for SVM and ANNs successfully solved the tool wear recognition problem in the full life span of the cutting tool.
引文
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