目前,国内大多数自来水厂采用的是一种基于PLC的集散式(DCS,Distributed Control System)控制系统。这里介绍构建基于Lorworks的水厂管控一体化网络的方法和途径,包括前端测控设备的配置、智能节点的配置和Neuron C编程。最后给出输入...目前,国内大多数自来水厂采用的是一种基于PLC的集散式(DCS,Distributed Control System)控制系统。这里介绍构建基于Lorworks的水厂管控一体化网络的方法和途径,包括前端测控设备的配置、智能节点的配置和Neuron C编程。最后给出输入输出控制程序,供参考。这是一种真正全分布式管控一体化网络的前端智能节点配置与设计方案。展开更多
A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant inf...A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.展开更多
文摘目前,国内大多数自来水厂采用的是一种基于PLC的集散式(DCS,Distributed Control System)控制系统。这里介绍构建基于Lorworks的水厂管控一体化网络的方法和途径,包括前端测控设备的配置、智能节点的配置和Neuron C编程。最后给出输入输出控制程序,供参考。这是一种真正全分布式管控一体化网络的前端智能节点配置与设计方案。
基金The National Natural Science Foundation of China(No.60443007,50390063).
文摘A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.
