基于多参量信息融合的刀具磨损状态识别及预测技术研究
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摘要
本文来源于国家重点基础研究发展计划分课题“大型动力装备制造基础研究”(2007CB707703-4)。在深入分析当前刀具状态监测技术研究成果和现状的基础上,针对存在的问题开展了一系列的研究。首先,科学地设计了试验方案,对不同切削条件下数控车削加工中切削力、振动、声发射、切削温度信号进行了刀具全寿命周期的实时采集,采用近似联合对角化下的总体经验模态分解(J-EEMD)算法对观测信号进行刀具磨损状态的特征提取,并在用神经网络进行模式识别的基础上,应用基于支持向量机的刀具磨损融合技术实现了对刀具磨损状态的二次决策识别,实验结果证明,该方法具有良好的识别率和鲁棒性。本文还应用灰色-隐马尔可夫模型对刀具磨损状态进行了科学的预测。
本文开展了以下研究工作：
(1)为了对基于多参量信息融合的切削刀具磨损状态规律进行研究,选用测力仪、陶瓷加速度计、红外热像仪、声发射传感器及数字采集系统等搭建了试验平台,建立了能够适时及监测数控车削加工过程中切削力、振动、切削热和声发射信号的刀具磨损状态监测系统。对加工过程中刀具全生命周期切削状态进行实时监控,为信号特征的提取、模式识别和刀具状态预测提供了科学依据。
(2)采用近似联合对角化下的总体经验模态分解(J-EEMD)方法对观测信号进行处理,该方法基于信号本身特征,自适应地将原振动信号和声发射分解为多个内蕴模式函数(IMF),然后根据各个IMF之间的能量比对变换,提取出了不同磨损状态下的刀具状态特征。实验证明,在该方法对测得数据进行处理的基础上,能够很好地识别出刀具磨损程度的不同状态。并通过对BP网络和Elman网络的训练实现了对其磨损状态特征的模式识别。
(3)针对常用的贝叶斯算法和D-S证据论的局限性提出了基于支持向量机的决策融合方法,接着利用所测数据,在BP和Elman神经网络识别结果的基础上,利用该方法实现了决策融合。实验结果证明,基于支持向量机的决策融合方法具有良好的识别率和鲁棒性,且比单用某一种网络节省时间。
(4)建立了反映数控车削加工刀具磨损状态的灰色-隐马尔可夫模型。以反映刀具磨损状态的特征值为输入数据,计算出刀具磨损状态的总体变化趋势的特征值,进而以此为依据利用所建立模型对刀具下一时刻所处的状态进行预测。实验结果表明,本方法有效可行,能对刀具下一时刻的状态做出准确地判定。以此模型建立实时监测预测系统,可以减少停机时间,实现最大经济效益。
The research of this thesis comes from National Key Basic Research Program of China (973):"Research on large power equipment manufacturing"(2007CB707703-4). Through the review and analysis of the present research situation of tool condition monitoring, a series of studies have been conducted aiming at existing problems. First, the experimental program was scientifically designed. Cutting force, vibration, acoustic emission and cutting temperature signals were collected in real-time, under the different CNC cutting conditions. The research was carry out to tool wear state signal feature extraction and pattern recognition, using the method of the Joint Approximate Diagonalization of Eigenmatrices based Ensemble Empirical Mode Decomposition (J-EEMD), Artificial Neural Network(ANN) and Support Vector Machine(SVM) decision fusion technology. In particular, the tool wear states have been scientifically predicted, applying gray-hidden Markov model.
This paper carried out the following research work:
(1) The experimental platform was set up using a dynamometer, ceramic accelerometer, infrared cameras, acoustic emission sensors and digital acquisition systems, in order to study cutting tool wear state monitoring that based on the integration of multi-parameter information. The monitoring system can timely monitor the signals of cutting force, vibration, cutting heat and acoustic emission in CNC turning process. After monitoring the entire life-cycle of tool wear states, the scientific basis is provided for signal feature extraction, pattern recognition, and tool state prediction.
(2) Observed signals were processed using the method of J-EEMD. This method is based on the characteristics of the signal itself decomposed into several Intrinsic Mode Functions (IMF), and then transform the energy ratio between the IMF, the original vibration signals and acoustic emission adaptive tool state characteristics under different wearing can be extracted. These experiments show that the method can identify the different states of tool wear based on the measured data. Tool wear state can be recognized by BP network and Elman network training.
(3) Decision fusion method based on support vector machine was proposed for the limitations of commonly used Bayesian algorithms and D-S evidence theory. The decision fusion can be achieved based on the recognition results of BP and Elman network. Experimental results show that decision fusion method based on support vector machine has a good recognition rate and robustness. At the same time, this approach saves time than single neural network.
(4) Gray-hidden Markov model is established based on the tool wear characteristics. A tool wear state of the next time is predicted through the prediction of the eigenvalues of the follow-up state of the tool. Experimental results show that the present method is feasible and effective, can accurately predict the next moment state of the tool. Real-time detection prediction system based on this model can reduce the downtime of CNC machine, and achieve the maximum economic benefits.
本文开展了以下研究工作：
(1)为了对基于多参量信息融合的切削刀具磨损状态规律进行研究,选用测力仪、陶瓷加速度计、红外热像仪、声发射传感器及数字采集系统等搭建了试验平台,建立了能够适时及监测数控车削加工过程中切削力、振动、切削热和声发射信号的刀具磨损状态监测系统。对加工过程中刀具全生命周期切削状态进行实时监控,为信号特征的提取、模式识别和刀具状态预测提供了科学依据。
(2)采用近似联合对角化下的总体经验模态分解(J-EEMD)方法对观测信号进行处理,该方法基于信号本身特征,自适应地将原振动信号和声发射分解为多个内蕴模式函数(IMF),然后根据各个IMF之间的能量比对变换,提取出了不同磨损状态下的刀具状态特征。实验证明,在该方法对测得数据进行处理的基础上,能够很好地识别出刀具磨损程度的不同状态。并通过对BP网络和Elman网络的训练实现了对其磨损状态特征的模式识别。
(3)针对常用的贝叶斯算法和D-S证据论的局限性提出了基于支持向量机的决策融合方法,接着利用所测数据,在BP和Elman神经网络识别结果的基础上,利用该方法实现了决策融合。实验结果证明,基于支持向量机的决策融合方法具有良好的识别率和鲁棒性,且比单用某一种网络节省时间。
(4)建立了反映数控车削加工刀具磨损状态的灰色-隐马尔可夫模型。以反映刀具磨损状态的特征值为输入数据,计算出刀具磨损状态的总体变化趋势的特征值,进而以此为依据利用所建立模型对刀具下一时刻所处的状态进行预测。实验结果表明,本方法有效可行,能对刀具下一时刻的状态做出准确地判定。以此模型建立实时监测预测系统,可以减少停机时间,实现最大经济效益。
The research of this thesis comes from National Key Basic Research Program of China (973):"Research on large power equipment manufacturing"(2007CB707703-4). Through the review and analysis of the present research situation of tool condition monitoring, a series of studies have been conducted aiming at existing problems. First, the experimental program was scientifically designed. Cutting force, vibration, acoustic emission and cutting temperature signals were collected in real-time, under the different CNC cutting conditions. The research was carry out to tool wear state signal feature extraction and pattern recognition, using the method of the Joint Approximate Diagonalization of Eigenmatrices based Ensemble Empirical Mode Decomposition (J-EEMD), Artificial Neural Network(ANN) and Support Vector Machine(SVM) decision fusion technology. In particular, the tool wear states have been scientifically predicted, applying gray-hidden Markov model.
This paper carried out the following research work:
(1) The experimental platform was set up using a dynamometer, ceramic accelerometer, infrared cameras, acoustic emission sensors and digital acquisition systems, in order to study cutting tool wear state monitoring that based on the integration of multi-parameter information. The monitoring system can timely monitor the signals of cutting force, vibration, cutting heat and acoustic emission in CNC turning process. After monitoring the entire life-cycle of tool wear states, the scientific basis is provided for signal feature extraction, pattern recognition, and tool state prediction.
(2) Observed signals were processed using the method of J-EEMD. This method is based on the characteristics of the signal itself decomposed into several Intrinsic Mode Functions (IMF), and then transform the energy ratio between the IMF, the original vibration signals and acoustic emission adaptive tool state characteristics under different wearing can be extracted. These experiments show that the method can identify the different states of tool wear based on the measured data. Tool wear state can be recognized by BP network and Elman network training.
(3) Decision fusion method based on support vector machine was proposed for the limitations of commonly used Bayesian algorithms and D-S evidence theory. The decision fusion can be achieved based on the recognition results of BP and Elman network. Experimental results show that decision fusion method based on support vector machine has a good recognition rate and robustness. At the same time, this approach saves time than single neural network.
(4) Gray-hidden Markov model is established based on the tool wear characteristics. A tool wear state of the next time is predicted through the prediction of the eigenvalues of the follow-up state of the tool. Experimental results show that the present method is feasible and effective, can accurately predict the next moment state of the tool. Real-time detection prediction system based on this model can reduce the downtime of CNC machine, and achieve the maximum economic benefits.
引文
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