Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significa...Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.展开更多
The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge...The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.展开更多
通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄...通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄。提出用非线性时序模型与前向神经网络相结合的模型(Nonlinear auto-regressive moving average neural network with exogenousinputs,NARMAX-NN)来辨识热弹性效应。用NARMAX-NN模型对高速进给系统试验台的热动态特性进行建模,获得良好的效果。此方法比多变量回归模型、反馈神经网络模型及广义最小二乘输出误差模型有更好的精度和鲁棒性,能精确地对复杂结构、多热源的时变非线性热误差特性进行建模和预测。展开更多
提出了一种软件系统的非线性有源自回归(Nonlinear AutoRegressive models with eXogenous Inputs,NARX)网络模型的老化检测方法。解决了目前软件老化方法未考虑多变量间关联性及历史数据的延迟影响的问题。该方法首先通过对实验采集的H...提出了一种软件系统的非线性有源自回归(Nonlinear AutoRegressive models with eXogenous Inputs,NARX)网络模型的老化检测方法。解决了目前软件老化方法未考虑多变量间关联性及历史数据的延迟影响的问题。该方法首先通过对实验采集的HelixServer-VOD服务器性能数据进行主成分分析,确定网络的输入维数,根据AIC准则确定最佳模型阶数,最终选取合理的网络模型结构;使用已知的未老化状态样本对NARX网络进行训练,建立系统的辨识模型;然后运用序贯概率比检验(Sequential Probability Ratio Test,SPRT)对NARX辨识模型的残差进行假设检验,判断系统的老化状态。实验分析表明,基于NARX网络模型的故障检测方法能够有效地应用于软件老化的检测。展开更多
利用1961—2015年国家气象信息中心沈阳站的日平均气温资料、美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的多变量ENSO指数(multivariate ENSO index,MEI)资料等,在分析沈阳地区气温月际变化...利用1961—2015年国家气象信息中心沈阳站的日平均气温资料、美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的多变量ENSO指数(multivariate ENSO index,MEI)资料等,在分析沈阳地区气温月际变化的基础上,结合厄尔尼诺/拉尼娜事件对其影响特征,利用线性倾向估计和非线性自回归(nonlinear auto regressive models with exogenous inputs,NARX)神经网络模型分别对沈阳地区2011—2015年的气温进行预测。结果表明,1961—2015年共计660个月中,沈阳地区11月—3月气温的变异系数在20%以上,远大于其他月份。1961—2015年的厄尔尼诺/拉尼娜事件往往在秋冬季达到最大强度,或为导致沈阳地区11月—3月气温变异增强的原因之一。厄尔尼诺事件结束之后的春季,沈阳地区气温偏低的概率逾70%。沈阳地区气温随MEI变化的线性倾向值为0.98,决定系数为0.98且通过了0.01的可信度检验。利用MEI对沈阳地区的气温进行同期和时滞预测,NARX的预测结果均优于一元线性回归模型。当气温滞后MEI16个月时,两者的相关系数达到最大且通过了0.01的显著性检验,此时回归模型预测的相关系数为0.59,较同期预测提升了79%;NARX预测的均方误差(mean-square error,MSE)为0.49,较同期预测降低了36%,相关系数为0.86,较同期预测提升了8%。展开更多
文摘Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.
基金This work was financially supported by the National Natural Science Foundation of China(Nos.51978589,51778544,and 51525804).
文摘The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.
文摘通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄。提出用非线性时序模型与前向神经网络相结合的模型(Nonlinear auto-regressive moving average neural network with exogenousinputs,NARMAX-NN)来辨识热弹性效应。用NARMAX-NN模型对高速进给系统试验台的热动态特性进行建模,获得良好的效果。此方法比多变量回归模型、反馈神经网络模型及广义最小二乘输出误差模型有更好的精度和鲁棒性,能精确地对复杂结构、多热源的时变非线性热误差特性进行建模和预测。
文摘提出了一种软件系统的非线性有源自回归(Nonlinear AutoRegressive models with eXogenous Inputs,NARX)网络模型的老化检测方法。解决了目前软件老化方法未考虑多变量间关联性及历史数据的延迟影响的问题。该方法首先通过对实验采集的HelixServer-VOD服务器性能数据进行主成分分析,确定网络的输入维数,根据AIC准则确定最佳模型阶数,最终选取合理的网络模型结构;使用已知的未老化状态样本对NARX网络进行训练,建立系统的辨识模型;然后运用序贯概率比检验(Sequential Probability Ratio Test,SPRT)对NARX辨识模型的残差进行假设检验,判断系统的老化状态。实验分析表明,基于NARX网络模型的故障检测方法能够有效地应用于软件老化的检测。
文摘极限学习机(Extreme learning machine,ELM)是一种单隐层前馈神经网络(SLFNs),它随机选择网络的隐含层节点及其参数,训练时仅需调节输出层权值,因此ELM以极快的学习速度获得良好的推广性。考虑到ELM的特征映射函数未知时,可以将核矩阵引入到ELM中。针对模型未知的强非线性连续搅拌反应釜(Continuous Stirred Tank Reactor,CSTR),提出一种基于核极限学习机(Extreme Learning Machine with Kernels,KELM)的NARX模型辨识方法。以仿真的CSTR过程实例进行辨识实验,建立基于NARX-KELM的辨识模型。实验结果表明,在相同条件下,与带动量因子的BP神经网络、模糊神经网络(FNN)、GAP-RBF、MGAP-RBF神经网络、回声状态网络(ESN)、ELM等方法相比,KELM能够有效地改进辨识精度,而且性能更好,这表明了所提方法的有效性和应用潜力。
文摘利用1961—2015年国家气象信息中心沈阳站的日平均气温资料、美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的多变量ENSO指数(multivariate ENSO index,MEI)资料等,在分析沈阳地区气温月际变化的基础上,结合厄尔尼诺/拉尼娜事件对其影响特征,利用线性倾向估计和非线性自回归(nonlinear auto regressive models with exogenous inputs,NARX)神经网络模型分别对沈阳地区2011—2015年的气温进行预测。结果表明,1961—2015年共计660个月中,沈阳地区11月—3月气温的变异系数在20%以上,远大于其他月份。1961—2015年的厄尔尼诺/拉尼娜事件往往在秋冬季达到最大强度,或为导致沈阳地区11月—3月气温变异增强的原因之一。厄尔尼诺事件结束之后的春季,沈阳地区气温偏低的概率逾70%。沈阳地区气温随MEI变化的线性倾向值为0.98,决定系数为0.98且通过了0.01的可信度检验。利用MEI对沈阳地区的气温进行同期和时滞预测,NARX的预测结果均优于一元线性回归模型。当气温滞后MEI16个月时,两者的相关系数达到最大且通过了0.01的显著性检验,此时回归模型预测的相关系数为0.59,较同期预测提升了79%;NARX预测的均方误差(mean-square error,MSE)为0.49,较同期预测降低了36%,相关系数为0.86,较同期预测提升了8%。
