现代工业过程往往具有多个运行模态,并且单一模态中的变量服从高斯与非高斯混合的复杂数据分布。针对多模态与复杂数据分布问题,基于局部离群概率(local outlier probability,LOOP)算法与支持向量数据描述(support vector data descript...现代工业过程往往具有多个运行模态,并且单一模态中的变量服从高斯与非高斯混合的复杂数据分布。针对多模态与复杂数据分布问题,基于局部离群概率(local outlier probability,LOOP)算法与支持向量数据描述(support vector data description,SVDD)算法,提出了一种名为MSVDD(multiple support vector data description,MSVDD)的多模态过程监控方法。首先,考虑到不同模态之间存在差异,利用差分策略以及局部离群概率算法对多模态数据进行聚类。其次,在每个单一模态下分别建立SVDD模型。然后,通过计算测试样本对每个单一模态的离群概率选择合适的模型进行过程监控。最后,在Tennessee Eastman(TE)平台上进行仿真测试以验证提出方法的可行性与有效性。展开更多
传统的多模态过程故障等级评估方法对模态之间的共性特征考虑较少,导致当被评估模态故障信息不充分时,评估的准确性较低.针对此问题,首先,提出一种共性–个性深度置信网络(Common and specific deep belief network,CS-DBN),该网络充分...传统的多模态过程故障等级评估方法对模态之间的共性特征考虑较少,导致当被评估模态故障信息不充分时,评估的准确性较低.针对此问题,首先,提出一种共性–个性深度置信网络(Common and specific deep belief network,CS-DBN),该网络充分利用深度置信网络(Deep belief network,DBN)的深度分层特征提取能力,通过度量多模态数据间分布的相似性和差异性,进一步得到能够反映多模态过程共有信息的共性特征以及反映每个模态独有信息的个性特征;其次,基于CS-DBN,利用多模态过程的已知故障等级数据生成多模态共性–个性特征集,通过加权逻辑回归构建故障等级评估模型;最后,将所提方法应用于带钢热连轧生产过程的故障等级评估中.应用结果表明,随着多模态故障等级数据的增加,所提方法的评估准确率逐渐增加,当故障信息充足时,评估准确率可达98.75%;故障信息不足时,与传统方法相比,评估准确率提升近10%.展开更多
Due to higher demands on product diversity,flexible shift between productions of different products in one equipment becomes a popular solution,resulting in existence of multiple operation modes in a single process.In...Due to higher demands on product diversity,flexible shift between productions of different products in one equipment becomes a popular solution,resulting in existence of multiple operation modes in a single process.In order to handle such multi-mode process,a novel double-layer structure is proposed and the original data are decomposed into common and specific characteristics according to the relationship between variables among each mode.In addition,both low and high order information are considered in each layer.The common and specific information within each mode can be captured and separated into several subspaces according to the different order information.The performance of the proposed method is further validated through a numerical example and the Tennessee Eastman(TE)benchmark.Compared with previous methods,superiority of the proposed method is validated by the better monitoring results.展开更多
文摘现代工业过程往往具有多个运行模态,并且单一模态中的变量服从高斯与非高斯混合的复杂数据分布。针对多模态与复杂数据分布问题,基于局部离群概率(local outlier probability,LOOP)算法与支持向量数据描述(support vector data description,SVDD)算法,提出了一种名为MSVDD(multiple support vector data description,MSVDD)的多模态过程监控方法。首先,考虑到不同模态之间存在差异,利用差分策略以及局部离群概率算法对多模态数据进行聚类。其次,在每个单一模态下分别建立SVDD模型。然后,通过计算测试样本对每个单一模态的离群概率选择合适的模型进行过程监控。最后,在Tennessee Eastman(TE)平台上进行仿真测试以验证提出方法的可行性与有效性。
文摘传统的多模态过程故障等级评估方法对模态之间的共性特征考虑较少,导致当被评估模态故障信息不充分时,评估的准确性较低.针对此问题,首先,提出一种共性–个性深度置信网络(Common and specific deep belief network,CS-DBN),该网络充分利用深度置信网络(Deep belief network,DBN)的深度分层特征提取能力,通过度量多模态数据间分布的相似性和差异性,进一步得到能够反映多模态过程共有信息的共性特征以及反映每个模态独有信息的个性特征;其次,基于CS-DBN,利用多模态过程的已知故障等级数据生成多模态共性–个性特征集,通过加权逻辑回归构建故障等级评估模型;最后,将所提方法应用于带钢热连轧生产过程的故障等级评估中.应用结果表明,随着多模态故障等级数据的增加,所提方法的评估准确率逐渐增加,当故障信息充足时,评估准确率可达98.75%;故障信息不足时,与传统方法相比,评估准确率提升近10%.
基金the National Natural Science Foundation of China(61903352)China Postdoctoral Science Foundation(2020M671721)+4 种基金Zhejiang Province Natural Science Foundation of China(LQ19F030007)Natural Science Foundation of Jiangsu Province(BK20180594)Project of department of education of Zhejiang province(Y202044960)Project of Zhejiang Tongji Vocational College of Science and Technology(TRC1904)Foundation of Key Laboratory of Advanced Process Control for Light Industry(Jiangnan University),Ministry of Education,P.R.China,APCLI1803.
文摘Due to higher demands on product diversity,flexible shift between productions of different products in one equipment becomes a popular solution,resulting in existence of multiple operation modes in a single process.In order to handle such multi-mode process,a novel double-layer structure is proposed and the original data are decomposed into common and specific characteristics according to the relationship between variables among each mode.In addition,both low and high order information are considered in each layer.The common and specific information within each mode can be captured and separated into several subspaces according to the different order information.The performance of the proposed method is further validated through a numerical example and the Tennessee Eastman(TE)benchmark.Compared with previous methods,superiority of the proposed method is validated by the better monitoring results.
