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Furong Gao - One of the best experts on this subject based on the ideXlab platform.
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Batch Process Modeling and Monitoring With Local Outlier Factor
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Jinlin Zhu, Youqing Wang, Donghua Zhou, Furong GaoAbstract:Batch Processes are commonly involved by a succession of working phases with implicit non-Gaussian behaviors. Besides, in most cases, Batch-to-Batch Processes also show similar but yet not identical running trajectory variations. To deal with these issues, this paper introduces a systematic analysis flowchart based on local outlier factor (LOF) for monitoring multiphase Batch Processes. First, a step-wise phase dividing algorithm is proposed with LOF to conduct phase dividing for a better understanding of Batch Process. Afterward, we develop the multiphase LOF for similar Batch data modeling and then fault detection. The fault isolation method is proposed, where variable contributions with LOFs are induced, also with the analysis of isolability. The developed method is validated on a numerical example and the fed-Batch fermentation benchmark Process, both of which are compared with the multiphase principal component analysis method. Results demonstrate the feasibility and superiority of the proposed method.
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two directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with uneven lengths
Chemical Engineering Science, 2018Co-Authors: Shumei Zhang, Furong Gao, Chunhui ZhaoAbstract:Abstract In general, Batch Processes cover two-directional dynamics, in which the Batch-wise dynamics are related to different operation modes, while the time-wise variations correspond to different phases within each Batch. The problem of unevenness is common as a result of various factors, particularly in multimode Batch Processes. In order to address these issues, this paper proposes a two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with the uneven problem. Firstly, pseudo time-slices are constructed in order to describe the Process characteristics regarding the sample concerned, which can preserve the local neighborhood information within a constrained searching range and effectively prevent the synchronizing problem caused by uneven lengths. Secondly, mode identification is conducted along the Batch direction and the phase affiliation is sequentially determined along time direction by determining the changes in variable correlations. The two-directional steps are implemented alternatively in order to identify the mode and phase information, which can also guarantee the time sequence within each mode. Thirdly, for online monitoring, the mode information and phase affiliation are simultaneously judged in real time for each new sample, from which the fault status is distinguished from the phase shift. The division results can indicate the critical-to-mode phases from which a certain mode begins to be separated into different sub-modes. In order to illustrate the feasibility and effectiveness of the proposed algorithm, it is applied to a multimode and multiphase Batch Process (namely an injection molding Process) with the uneven problem.
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an iterative two step sequential phase partition itspp method for Batch Process modeling and online monitoring
Aiche Journal, 2016Co-Authors: Yan Qin, Chunhui Zhao, Furong GaoAbstract:Operating at different manufacturing steps, multiphase modeling and analysis of the Batch Process are advantageous to improving monitoring performance and understanding manufacturing Processes. Although many phase partition algorithms have been proposed, they have some disadvantages and cause problems: (1) time sequence disorder, which requires elaborate post-treatments; (2) a lack of quantitative index to indicate transition patterns; and (3) tunable parameters that cannot be quantitatively determined. To effectively overcome these problems, an iterative two-step sequential phase partition algorithm is proposed in the present work. In the first step, initial phase partition results are obtained by checking changes of the control limit of squared prediction error. Sequentially, the fast search and find of density peaks clustering algorithm is employed to adjust the degradation degree and update the phase partition results. These two steps are iteratively executed until a proper degradation degree is found for the first phase. Then, the remaining phases are Processed one by one using the same procedure. Moreover, a statistical index is quantitatively defined based on density and distance analysis to judge whether a Process has transitions, and when the transition regions begin and end. In this way, the phases and transition patterns are quantitatively determined without ambiguity from the perspective of monitoring performance. The effectiveness of the proposed method is illustrated by a numerical example and a typical industrial case. Several typical phase partition algorithms are also employed for comprehensive comparisons with the proposed method. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2358–2373, 2016
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Batch Process Control
Industrial Process Identification and Control Design, 2011Co-Authors: Tao Liu, Furong GaoAbstract:Batch Processes have been widely applied in modern industries to manufacture a large quantity of products with good consistency and high efficiency. Typical Batch Processes include robotic manipulators, semiconductor product lines, injection molding, pharmaceutical crystallization, etc. Generally, a Batch Process is defined as “a Process that leads to the production of finite quantities of material by subjecting quantities of input materials to an ordered set of Processing activities over a finite period of time using one or more pieces of equipment” (Instrument Society of America 1995).
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Batch Process monitoring based on support vector data description method
Journal of Process Control, 2011Co-Authors: Furong Gao, Zhihuan SongAbstract:Abstract Process monitoring can be considered as a one-class classification problem, the aim of which is to differentiate the normal data samples from the faulty ones. This paper introduces an efficient one-class classification method for Batch Process monitoring, which is called support vector data description (SVDD). Different from the traditional data description method such as principal component analysis (PCA) and partial least squares (PLS), SVDD has no Gaussian assumption of the Process data, and is also effective for nonlinear Process modeling. Furthermore, SVDD only incorporates a quadratic optimization step, which makes it easy for practical implementation. Based on the basic SVDD Batch Process monitoring approach, the method is further extended to multiphase and multimode Batch Processes. Two case studies are provided to evaluate the monitoring performance of the proposed methods.
Chunhui Zhao - One of the best experts on this subject based on the ideXlab platform.
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Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Shumei Zhang, Chunhui ZhaoAbstract:In order to provide more sensitive monitoring results, the time dynamics and steady-state operating conditions should be separately monitored by distinguishing time information from the steady-state counterpart. However, it is a more challenging task for Batch Processes because they vary from phase to phase presenting multiple steady states and complex dynamic characteristics. To address the above issue, a concurrent monitoring strategy of multiphase steady states and Process dynamics is developed for Batch Processes in this paper. On one hand, multiple local models are constructed to identify a steady derivation from the normal operating condition for different phases. On the other hand, based on the recognition that the Process dynamics can be considered to be irrelevant with the steady states, a global model is built to detect the dynamics anomalies by monitoring the time variations. Corresponding to alarms issued by different statistics, different operating statuses are indicated with meaningful physical interpretation and deep Process understanding. To illustrate the feasibility and efficacy, the proposed algorithm is applied to the injection molding Process, which is a typical multiphase Batch Process.
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two directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with uneven lengths
Chemical Engineering Science, 2018Co-Authors: Shumei Zhang, Furong Gao, Chunhui ZhaoAbstract:Abstract In general, Batch Processes cover two-directional dynamics, in which the Batch-wise dynamics are related to different operation modes, while the time-wise variations correspond to different phases within each Batch. The problem of unevenness is common as a result of various factors, particularly in multimode Batch Processes. In order to address these issues, this paper proposes a two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with the uneven problem. Firstly, pseudo time-slices are constructed in order to describe the Process characteristics regarding the sample concerned, which can preserve the local neighborhood information within a constrained searching range and effectively prevent the synchronizing problem caused by uneven lengths. Secondly, mode identification is conducted along the Batch direction and the phase affiliation is sequentially determined along time direction by determining the changes in variable correlations. The two-directional steps are implemented alternatively in order to identify the mode and phase information, which can also guarantee the time sequence within each mode. Thirdly, for online monitoring, the mode information and phase affiliation are simultaneously judged in real time for each new sample, from which the fault status is distinguished from the phase shift. The division results can indicate the critical-to-mode phases from which a certain mode begins to be separated into different sub-modes. In order to illustrate the feasibility and effectiveness of the proposed algorithm, it is applied to a multimode and multiphase Batch Process (namely an injection molding Process) with the uneven problem.
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pseudo time slice construction using a variable moving window k nearest neighbor rule for sequential uneven phase division and Batch Process monitoring
Industrial & Engineering Chemistry Research, 2017Co-Authors: Shumei Zhang, Chunhui Zhao, Shu Wang, Fuli WangAbstract:Multiphase characteristics and uneven-length Batch duration have been two critical issues to be addressed for Batch Process monitoring. To handle these issues, a variable moving window-k nearest neighbor (VMW-kNN) based local modeling, irregular phase division, and monitoring strategy is proposed for uneven Batch Processes in the present paper. First, a pseudo time-slice is constructed for each sample by searching samples that are closely similar to the concerned sample in which the variable moving window (VMW) strategy is adopted to vary the searching range and the k nearest neighbor (kNN) rule is used to find the similar samples. Second, a novel automatic sequential phase division procedure is proposed by similarity evaluation for local models derived from pseudo time-slices to get different irregular phases and ensure their time sequence. Third, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. The pr...
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an iterative two step sequential phase partition itspp method for Batch Process modeling and online monitoring
Aiche Journal, 2016Co-Authors: Yan Qin, Chunhui Zhao, Furong GaoAbstract:Operating at different manufacturing steps, multiphase modeling and analysis of the Batch Process are advantageous to improving monitoring performance and understanding manufacturing Processes. Although many phase partition algorithms have been proposed, they have some disadvantages and cause problems: (1) time sequence disorder, which requires elaborate post-treatments; (2) a lack of quantitative index to indicate transition patterns; and (3) tunable parameters that cannot be quantitatively determined. To effectively overcome these problems, an iterative two-step sequential phase partition algorithm is proposed in the present work. In the first step, initial phase partition results are obtained by checking changes of the control limit of squared prediction error. Sequentially, the fast search and find of density peaks clustering algorithm is employed to adjust the degradation degree and update the phase partition results. These two steps are iteratively executed until a proper degradation degree is found for the first phase. Then, the remaining phases are Processed one by one using the same procedure. Moreover, a statistical index is quantitatively defined based on density and distance analysis to judge whether a Process has transitions, and when the transition regions begin and end. In this way, the phases and transition patterns are quantitatively determined without ambiguity from the perspective of monitoring performance. The effectiveness of the proposed method is illustrated by a numerical example and a typical industrial case. Several typical phase partition algorithms are also employed for comprehensive comparisons with the proposed method. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2358–2373, 2016
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Concurrent phase partition and between‐mode statistical analysis for multimode and multiphase Batch Process monitoring
Aiche Journal, 2013Co-Authors: Chunhui ZhaoAbstract:The exiting automatic phase partition and phase-based Process monitoring strategies are in general limited to single-mode multiphase Batch Processes. In this article, a concurrent phase partition and between-mode statistical modeling strategy (CPPBM) is proposed for online monitoring of multimode multiphase Batch Processes. First, the time-varying characteristics of Batch Processes are concurrently analyzed across modes so that multiple sequential phases are simultaneously identified for all modes. The feature is that both time-wise dynamics and mode-wise variations are considered to get the consistent phase boundaries. Then within each phase, between-mode statistical analysis is performed where one mode is chosen for the development of reference monitoring system and the relative changes from the reference mode to each alternative mode are analyzed. From the between-mode perspective, each of the original reference monitoring subspaces, including systematic subspace and residual subspace, are further decomposed into two monitoring subspaces for each alternative mode, which reveal two kinds of between-mode relative variations. The part which shows significant increases represents the variations that will cause alarm signals if the reference models are used to monitor the alternative modes, whereas the part that shows no increases will not issue alarms. By modeling and monitoring different types of between-mode relative variations, the proposed CPPBM method can not only efficiently detect faults but also offer enhanced Process understanding. It is illustrated with a typical multiphase Batch Process with multiple modes. © 2013 American Institute of Chemical Engineers AIChE J 60: 559–573, 2014
Shumei Zhang - One of the best experts on this subject based on the ideXlab platform.
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Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Shumei Zhang, Chunhui ZhaoAbstract:In order to provide more sensitive monitoring results, the time dynamics and steady-state operating conditions should be separately monitored by distinguishing time information from the steady-state counterpart. However, it is a more challenging task for Batch Processes because they vary from phase to phase presenting multiple steady states and complex dynamic characteristics. To address the above issue, a concurrent monitoring strategy of multiphase steady states and Process dynamics is developed for Batch Processes in this paper. On one hand, multiple local models are constructed to identify a steady derivation from the normal operating condition for different phases. On the other hand, based on the recognition that the Process dynamics can be considered to be irrelevant with the steady states, a global model is built to detect the dynamics anomalies by monitoring the time variations. Corresponding to alarms issued by different statistics, different operating statuses are indicated with meaningful physical interpretation and deep Process understanding. To illustrate the feasibility and efficacy, the proposed algorithm is applied to the injection molding Process, which is a typical multiphase Batch Process.
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two directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with uneven lengths
Chemical Engineering Science, 2018Co-Authors: Shumei Zhang, Furong Gao, Chunhui ZhaoAbstract:Abstract In general, Batch Processes cover two-directional dynamics, in which the Batch-wise dynamics are related to different operation modes, while the time-wise variations correspond to different phases within each Batch. The problem of unevenness is common as a result of various factors, particularly in multimode Batch Processes. In order to address these issues, this paper proposes a two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with the uneven problem. Firstly, pseudo time-slices are constructed in order to describe the Process characteristics regarding the sample concerned, which can preserve the local neighborhood information within a constrained searching range and effectively prevent the synchronizing problem caused by uneven lengths. Secondly, mode identification is conducted along the Batch direction and the phase affiliation is sequentially determined along time direction by determining the changes in variable correlations. The two-directional steps are implemented alternatively in order to identify the mode and phase information, which can also guarantee the time sequence within each mode. Thirdly, for online monitoring, the mode information and phase affiliation are simultaneously judged in real time for each new sample, from which the fault status is distinguished from the phase shift. The division results can indicate the critical-to-mode phases from which a certain mode begins to be separated into different sub-modes. In order to illustrate the feasibility and effectiveness of the proposed algorithm, it is applied to a multimode and multiphase Batch Process (namely an injection molding Process) with the uneven problem.
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pseudo time slice construction using a variable moving window k nearest neighbor rule for sequential uneven phase division and Batch Process monitoring
Industrial & Engineering Chemistry Research, 2017Co-Authors: Shumei Zhang, Chunhui Zhao, Shu Wang, Fuli WangAbstract:Multiphase characteristics and uneven-length Batch duration have been two critical issues to be addressed for Batch Process monitoring. To handle these issues, a variable moving window-k nearest neighbor (VMW-kNN) based local modeling, irregular phase division, and monitoring strategy is proposed for uneven Batch Processes in the present paper. First, a pseudo time-slice is constructed for each sample by searching samples that are closely similar to the concerned sample in which the variable moving window (VMW) strategy is adopted to vary the searching range and the k nearest neighbor (kNN) rule is used to find the similar samples. Second, a novel automatic sequential phase division procedure is proposed by similarity evaluation for local models derived from pseudo time-slices to get different irregular phases and ensure their time sequence. Third, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. The pr...
Fuli Wang - One of the best experts on this subject based on the ideXlab platform.
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pseudo time slice construction using a variable moving window k nearest neighbor rule for sequential uneven phase division and Batch Process monitoring
Industrial & Engineering Chemistry Research, 2017Co-Authors: Shumei Zhang, Chunhui Zhao, Shu Wang, Fuli WangAbstract:Multiphase characteristics and uneven-length Batch duration have been two critical issues to be addressed for Batch Process monitoring. To handle these issues, a variable moving window-k nearest neighbor (VMW-kNN) based local modeling, irregular phase division, and monitoring strategy is proposed for uneven Batch Processes in the present paper. First, a pseudo time-slice is constructed for each sample by searching samples that are closely similar to the concerned sample in which the variable moving window (VMW) strategy is adopted to vary the searching range and the k nearest neighbor (kNN) rule is used to find the similar samples. Second, a novel automatic sequential phase division procedure is proposed by similarity evaluation for local models derived from pseudo time-slices to get different irregular phases and ensure their time sequence. Third, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. The pr...
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on line Batch Process monitoring using Batch dynamic kernel principal component analysis
Chemometrics and Intelligent Laboratory Systems, 2010Co-Authors: Fuli Wang, Wei WangAbstract:Abstract In this paper, a new dynamic and nonlinear Batch Process monitoring method, referred to as BDKPCA, is developed for on-line Batch Process monitoring, tactfully integrating kernel PCA and ARMAX time series model through estimating the Average Kernel Matrix (AKM) of all Batch runs. AKM is an average of I, the Batch number, Single-Batch Kernel Matrixes (SBKM). Each of the I SBKM is also an average of I kernel matrixes for each Batch. The AKM contains the information of the stochastic variations and deviations among Batches. This information will be very useful for the BDKPCA model to characterize the Batch Process in detail. The structure of BDKPCA model is very simple, and BDKPCA calculates the Hotelling's T2 statistic and the Q-statistic for every time point, enhancing the method's sensitivity to the faults. Two cases are used to investigate the potential application of the proposed method, and its application to on-line Batch Process monitoring shows better performance than MKPCA.
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two dimensional dynamic pca for Batch Process monitoring
Aiche Journal, 2005Co-Authors: Yuan Yao, Furong Gao, Fuli WangAbstract:Batch Processes play an important role in many industriesfor flexible manufacturing of high value-added products. On-line monitoring of Batch Processes is of critical importance inensuring operation safety and quality consistency. Multivariatestatistical methods for continuous Processes, such as principalcomponent analysis (PCA) and partial least square (PLS), havebeen extended to Batch Process monitoring with some suc-cesses, for example, multiway PCA/PLS,
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Two‐dimensional dynamic PCA for Batch Process monitoring
Aiche Journal, 2005Co-Authors: Yuan Yao, Furong Gao, Fuli WangAbstract:Batch Processes play an important role in many industriesfor flexible manufacturing of high value-added products. On-line monitoring of Batch Processes is of critical importance inensuring operation safety and quality consistency. Multivariatestatistical methods for continuous Processes, such as principalcomponent analysis (PCA) and partial least square (PLS), havebeen extended to Batch Process monitoring with some suc-cesses, for example, multiway PCA/PLS,
Wenny H M Raaymakers - One of the best experts on this subject based on the ideXlab platform.
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makespan estimation in Batch Process industries using aggregate resource and job set characteristics
International Journal of Production Economics, 2001Co-Authors: Wenny H M Raaymakers, Will J M Bertrand, J Jan C FransooAbstract:To properly conduct aggregate control functions such as order acceptance and capacity loading, good estimates of the available production capacity need to be on hand. Capacity structures in Batch Process industries are generally so complex that it is not straightforward to estimate the capacity of a production department. In this paper, we assess the quality of estimation models that are based on regression. The paper builds on earlier results, which have demonstrated that a limited number of factors can explain a large share of the variance in makespan estimation based on regression models.
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identification of aggregate resource and job set characteristics for predicting job set makespan in Batch Process industries
International Journal of Production Economics, 2000Co-Authors: Wenny H M Raaymakers, J Jan C FransooAbstract:We study multipurpose Batch Process industries with no-wait restrictions, overlapping Processing steps, and parallel resources. To achieve high utilization and reliable lead times, the master planner needs to be able to accurately and quickly estimate the makespan of a job set. Because constructing a schedule is time consuming, and production plans may change frequently, estimates must be based on aggregate characteristics of the job set. To estimate the makespan of a complex set of jobs, we introduce the concept of job interaction. Using statistical analysis, we show that a limited number of characteristics of the job set and the available resources can explain most of the variability in the job interaction.
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scheduling multipurpose Batch Process industries with no wait restrictions by simulated annealing
European Journal of Operational Research, 2000Co-Authors: Wenny H M Raaymakers, J A HoogeveenAbstract:Scheduling problems in multipurpose Batch Process industries are very hard to solve because of the job shop like Processing structure in combination with rigid technical constraints, such as no-wait restrictions. This paper shows that scheduling problems in this type of industry may be characterized as multiProcessor no-wait job shop problems with overlapping operations. A simulated annealing algorithm is proposed that obtains near-optimal solutions with respect to makespan. This paper shows that the no-wait restrictions require several adaptations of the neighborhood structure used by simulated annealing. The performance of the algorithm is evaluated by scheduling industrial instances from a multipurpose Batch plant in the pharmaceutical industry. Our results indicate that simulated annealing consistently gives better results for a number of realistic instances than simple heuristics within acceptable computation time.
