Nonlinear principal component analysis(NLPCA)fault detection method achieves good detection results especially in a nonlinear process.Signed directed graph(SDG)model is based on deep-going information,which excels in ...Nonlinear principal component analysis(NLPCA)fault detection method achieves good detection results especially in a nonlinear process.Signed directed graph(SDG)model is based on deep-going information,which excels in fault interpretation.In this work,an NLPCA-SDG fault diagnosis method was proposed.SDG model was used to interpret the residual contributions produced by NLPCA.This method could overcome the shortcomings of traditional principal component analysis(PCA)method in fault detection of a nonlinear process and the shortcomings of traditional SDG method in single variable statistics in discriminating node conditions and threshold values.The application to a distillation unit of a petrochemical plant illustrated its validity in nonlinear process fault diagnosis.展开更多
图划分是大图数据并行计算的基础,目前主要采用分布式算法实现大图划分.非易失存储器(Non-Volatile Memory,NVM)速度接近动态随机存储器(Dynamic Random Access Memory,DRAM),且具有低功耗、高密度、低时延等优点,本文针对分布式图划分...图划分是大图数据并行计算的基础,目前主要采用分布式算法实现大图划分.非易失存储器(Non-Volatile Memory,NVM)速度接近动态随机存储器(Dynamic Random Access Memory,DRAM),且具有低功耗、高密度、低时延等优点,本文针对分布式图划分算法难以分析和调试等问题,设计了基于混合内存的单机图划分算法框架.作者提出了基于邻边结构的图划分结果动态缓存管理策略(AeFdy),以提高缓存区邻居节点的搜索效率.在17种真实应用数据上的实验结果表明,采用新方法的平均图划分速度是基于邻点结构算法的4.9倍.本文还针对NVM寿命有限的问题,设计了基于内存页读写特征的迁移算法,实现了NVM写操作受限条件下的迁移优化方案.相对于Linux Swap、M-CLOCK、Dr.Swap混合内存管理策略,使用AeFdy策略的性能分别提升了128.5%、87.4%与50.4%.仿真实验结果表明,本文设计的混合内存管理方法实现了NVM+DRAM高效协同.展开更多
文摘Nonlinear principal component analysis(NLPCA)fault detection method achieves good detection results especially in a nonlinear process.Signed directed graph(SDG)model is based on deep-going information,which excels in fault interpretation.In this work,an NLPCA-SDG fault diagnosis method was proposed.SDG model was used to interpret the residual contributions produced by NLPCA.This method could overcome the shortcomings of traditional principal component analysis(PCA)method in fault detection of a nonlinear process and the shortcomings of traditional SDG method in single variable statistics in discriminating node conditions and threshold values.The application to a distillation unit of a petrochemical plant illustrated its validity in nonlinear process fault diagnosis.
