动态MPCA在发酵过程监测与故障诊断中的应用

针对发酵过程非线性和时变特点,提出了一种具有实时性的动态MPCA方法,采用多模型非线性结构代替传统MPCA单模型线性化结构,克服了后者不能处理非线性过程和实时性的问题,并避免了MPCA在线应用时预报未来测量值带来的误差,提高了发酵过程性能监测和故障诊断的准确性。对头孢菌素C发酵过程的拟在线仿真研究,验证了基于动态MPCA的统计过程监测的有效性。

基于MPCA与DTW的间歇反应过程的性能监视及故障诊断

研究了基于多方向主元分析(MPCA)理论和动态时间错位(DTW)理论对间歇反应过程进行性能监视及故障诊断的方法.多元统计分析理论是根据反应过程的历史批量数据,建立过程的统计数据库、统计数学模型;在此基础上确定过程的统计监控指标,并向各自的统计模型投影,判断其与模型的拟合程度来诊断是否有故障发生.由于间歇反应的各批次物理条件的不同、约束条件的不同,导致批次与批次之间的数据轨迹不同步.动态时间错位(DTW)理论通过搜寻两轨迹的相似特征,适时地对轨迹进行压缩和扩张,从而使其达到一致的程度.这为基于MPCA理论进行故障诊断提供了合理、可靠的数据依据.比较轨迹进行同步化处理前、后的故障诊断结果完全不同,因此,DTW理论的应用在基于MPCA理论进行性能监视及故障诊断中具有重要意义.

ASCS Online Fault Detection and Isolation Based on an Improved MPCA

Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.

基于多线性主成分分析的支持高阶张量机

为了处理张量数据,传统的学习算法常常把张量展成向量,但会造成破坏原始数据固有的高阶结构和内在相关性,导致信息丢失,或产生高维向量,使得后期学习过程中容易出现过拟合、维度灾难和小样本问题.近年提出了许多基于张量模式的分类算法,而支持高阶张量机算法是张量分类算法中最有效的方法之一.考虑到张量的高维性和高冗余性,本文提出基于多线性主成分分析的支持高阶张量机分类算法(Multilinear Principle Component Analysis Based Support High-Order Tensor Machine,MPCA+SHTM).该算法首先利用多线性主成分分析对张量进行降维,然后利用支持高阶张量机对降维后的张量进行学习.在12个张量数据集上的实验表明:MPCA+SHTM在保持测试精度的情况下有效地降低了SHTM的计算时间.

基于改进的MPCA与DTW方法及其在批过程故障诊断中的应用

针对传统的多向主元分析(Multiway Principal component Analysis,MPCA)常会导致误诊断,且对批生产过程难以保证在线状态监测和故障诊断的实时性,提出了一种改进的MPCA与动态时间错位(Dynamic Time Warping,DTW)方法,该方法采用多模型非线性结构代替传统的MPCA单模型线性化结构,并利用对称式DTW算法解决了多元轨迹同步化的问题。将该方法应用到青霉素发酵批过程的在线故障监测中,结果表明它克服了MPCA不能处理非线性过程和实时性问题,并避免了MPCA 在线应用时预报未来测量值带来的误差,提高了批过程性能监测和故障诊断的准确性。

中药生产多工序多指标统计质量控制(MMSQC)方法

目的:建立中药生产过程中各个工序产生的中间体质量监控方法,保证中药产品质量的批次一致性。方法:基于多变量数据分析技术,提出一种中药生产质量监控方法——多工序多指标统计质量控制(multistage multivariate statisticalquality control,MMSQC),采用Hotelling T2和SPE统计量,监控各个工序中间体的质量。以丹参注射液生产为例,介绍了MMSQC的应用方法。结果:MMSQC方法能监控多个工序产生的中间体质量,与单指标监控方法相比,更加简便、准确。结论:MMSQC方法可以推广应用于中药生产过程多工序的质量监控。