Online monitoring of chemical process performance is extremely important to ensure the safety of a chemical plant and consistently high quality of products. Multivariate statistical process control has found wide appl...Online monitoring of chemical process performance is extremely important to ensure the safety of a chemical plant and consistently high quality of products. Multivariate statistical process control has found wide applications in process performance analysis, monitoring and fault diagnosis using existing rich historical database.In this paper, we propose a simple and straight forward multivariate statistical modeling based on a moving window MPCA (multiway principal component analysis) model along the time and batch axis for adaptive monitoring the progress of batch processes in real-time. It is an extension to minimum window MPCA and traditional MPCA.The moving window MPCA along the batch axis can copy seamlessly with variable run length and does not need to estimate any deviations of the ongoing batch from the average trajectories. It replaces an invariant fixed-model monitoring approach with adaptive updating model data structure within batch-to-batch, which overcomes the changing operation condition and slows time-varying behaviors of industrial processes. The software based on moving window MPCA has been successfully applied to the industrial polymerization reactor of polyvinyl chloride (PVC) process in the Jinxi Chemical Company of China since 1999.展开更多
在研究多向主元分析(MPCA——Multi-way Principal Component Analysis)理论的基础上,通过对间歇过程数据的分析研究移动窗口多向主元分析(MWMPCA——Moving Window Multi-way Principal Component Analysis)理论,并将该方法应用于TE过...在研究多向主元分析(MPCA——Multi-way Principal Component Analysis)理论的基础上,通过对间歇过程数据的分析研究移动窗口多向主元分析(MWMPCA——Moving Window Multi-way Principal Component Analysis)理论,并将该方法应用于TE过程进行故障检测与诊断.与MPCA方法比较,MWMPCA方法随采样的增加窗口长度不断改变,使窗口内有用的信息不断增加,所建模型更加准确,能提高监控系统的稳定性.通过对Q统计量、HotellingT2统计量的检测结果进行分析比较,证明MWMPCA理论在检测系统异常事件中能提高系统的准确性,使系统故障检测与诊断的性能得到改进.展开更多
针对间歇过程固有的批次不等长问题,也为了克服传统解决批次间同步问题方法存在的数据浪费、扭曲原始过程变量的自相关及交叉相关关系的严重缺陷,提出基于多约束的动态时间规整(dynamic time warping,DTW)方法,按照轨迹中点与点的模式...针对间歇过程固有的批次不等长问题,也为了克服传统解决批次间同步问题方法存在的数据浪费、扭曲原始过程变量的自相关及交叉相关关系的严重缺陷,提出基于多约束的动态时间规整(dynamic time warping,DTW)方法,按照轨迹中点与点的模式进行动态匹配解决的同步问题.同时,引入了全局路径限制和失真度阈值限制对DTW方法进行改进,解决了传统DTW方法长时间运行造成的故障监测严重滞后的问题,同时克服了其处理过程的复杂性与其离线性导致其实际应用的困难.用多向主元分析(multiway principal component analysis,MPCA)方法将多约束DTW处理过的数据进行建模.将该方法应用到青霉素发酵过程仿真实验中,结果表明:该方法能够快速准确地对不等长批次进行规整,与传统方法相比,故障的误报率、漏报率明显降低.展开更多
An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction ste...An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.展开更多
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-dimensio...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.展开更多
A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensi...A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.展开更多
基金国家重点基础研究发展计划(973计划)，国家自然科学基金，the National Natural Science Foundation of China
文摘Online monitoring of chemical process performance is extremely important to ensure the safety of a chemical plant and consistently high quality of products. Multivariate statistical process control has found wide applications in process performance analysis, monitoring and fault diagnosis using existing rich historical database.In this paper, we propose a simple and straight forward multivariate statistical modeling based on a moving window MPCA (multiway principal component analysis) model along the time and batch axis for adaptive monitoring the progress of batch processes in real-time. It is an extension to minimum window MPCA and traditional MPCA.The moving window MPCA along the batch axis can copy seamlessly with variable run length and does not need to estimate any deviations of the ongoing batch from the average trajectories. It replaces an invariant fixed-model monitoring approach with adaptive updating model data structure within batch-to-batch, which overcomes the changing operation condition and slows time-varying behaviors of industrial processes. The software based on moving window MPCA has been successfully applied to the industrial polymerization reactor of polyvinyl chloride (PVC) process in the Jinxi Chemical Company of China since 1999.
文摘运动目标传统检测方法只考虑图像的亮度或纹理等某一种特性,受特异值影响较大,对噪声比较敏感,鲁棒性也不够好,而且背景恢复精度不高。针对以上局限性,提出一种融合结构相似度(structural similarity,SSIM)全参考模型和鲁棒主成分分析(robust principal component analysis,RPCA)的运动目标检测方法。此方法综合考虑图像的亮度、对比度和结构三种特性,不采用传统的背景减除法,而是把图像像素点的结构相似度作为度量来实现运动对象与背景的分离。实验结果表明,此方法准确率可达0.95,且F度量较传统运动目标检测算法平均提升0.15,总体上比传统方法更具优势。
文摘在研究多向主元分析(MPCA——Multi-way Principal Component Analysis)理论的基础上,通过对间歇过程数据的分析研究移动窗口多向主元分析(MWMPCA——Moving Window Multi-way Principal Component Analysis)理论,并将该方法应用于TE过程进行故障检测与诊断.与MPCA方法比较,MWMPCA方法随采样的增加窗口长度不断改变,使窗口内有用的信息不断增加,所建模型更加准确,能提高监控系统的稳定性.通过对Q统计量、HotellingT2统计量的检测结果进行分析比较,证明MWMPCA理论在检测系统异常事件中能提高系统的准确性,使系统故障检测与诊断的性能得到改进.
文摘针对间歇过程固有的批次不等长问题,也为了克服传统解决批次间同步问题方法存在的数据浪费、扭曲原始过程变量的自相关及交叉相关关系的严重缺陷,提出基于多约束的动态时间规整(dynamic time warping,DTW)方法,按照轨迹中点与点的模式进行动态匹配解决的同步问题.同时,引入了全局路径限制和失真度阈值限制对DTW方法进行改进,解决了传统DTW方法长时间运行造成的故障监测严重滞后的问题,同时克服了其处理过程的复杂性与其离线性导致其实际应用的困难.用多向主元分析(multiway principal component analysis,MPCA)方法将多约束DTW处理过的数据进行建模.将该方法应用到青霉素发酵过程仿真实验中,结果表明:该方法能够快速准确地对不等长批次进行规整,与传统方法相比,故障的误报率、漏报率明显降低.
基金Supported by National High-Tech Program of China (No. 2001AA413110).
文摘An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2011AA11A223)
文摘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.
文摘A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.
