Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learnin...Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product quality.Considering the slow time-varying characteristic of industrial processes,the model parameters should be updated smoothly.According to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as OAS-RVFLN.By introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter update.Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy.Finally,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.展开更多
提出一种用最小二乘支持向量机(least squares support vector machine,LS-SVM)构造函数链接型神经网络(functional link artificial neural networks,FLANN)的滚动轴承故障诊断系统。介绍了相关原理和具体算法,并给出了滚动轴承故障诊...提出一种用最小二乘支持向量机(least squares support vector machine,LS-SVM)构造函数链接型神经网络(functional link artificial neural networks,FLANN)的滚动轴承故障诊断系统。介绍了相关原理和具体算法,并给出了滚动轴承故障诊断系统模型。首先,采用LS-SVM模型核函数代替常规FLANN模型的扩展函数,避免了扩展函数选择的任意性;其次,利用LS-SVM学习模型得到FLANN权重系数,避免了BP方法多次迭代寻优存在的耗时长、局部极小及迭代设置初值依赖经验等不足;最后,构造了多层LS-SVM-FLANN结构,对多类滚动轴承故障进行诊断。具体实验表明,用LS-SVM构造FLANN的滚动轴承故障识别系统精度高、鲁棒性好、实现简单。展开更多
基金Projects(61603393,61973306)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Projects(2015M581885,2018T110571)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product quality.Considering the slow time-varying characteristic of industrial processes,the model parameters should be updated smoothly.According to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as OAS-RVFLN.By introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter update.Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy.Finally,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.
文摘提出一种用最小二乘支持向量机(least squares support vector machine,LS-SVM)构造函数链接型神经网络(functional link artificial neural networks,FLANN)的滚动轴承故障诊断系统。介绍了相关原理和具体算法,并给出了滚动轴承故障诊断系统模型。首先,采用LS-SVM模型核函数代替常规FLANN模型的扩展函数,避免了扩展函数选择的任意性;其次,利用LS-SVM学习模型得到FLANN权重系数,避免了BP方法多次迭代寻优存在的耗时长、局部极小及迭代设置初值依赖经验等不足;最后,构造了多层LS-SVM-FLANN结构,对多类滚动轴承故障进行诊断。具体实验表明,用LS-SVM构造FLANN的滚动轴承故障识别系统精度高、鲁棒性好、实现简单。