Statistical analysis and online monitoring for handling multiphase batch processes with varying durations

Zhao, Chunhui; Mo, Shengyong; Gao, Furong; Lu, Ningyun; Yao, Yuan

doi:10.1016/j.jprocont.2011.04.005

Cited by 51 publications

(36 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparatively, this fault cannot be detected until the middle of the injection phase using the method proposed in previous work. 19 The results are not shown here. The better fault detection performance may result from the use of inner-phase division where the transition patterns are separated from the steady part and they are then characterized more accurately.…”

Section: Model Development For Transitionsmentioning

confidence: 94%

Inner-Phase Analysis Based Statistical Modeling and Online Monitoring for Uneven Multiphase Batch Processes

Zhao

Gao

2013

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

The multiplicity of operation phases is inherent in the nature of many batch processes, and each phase exhibits significantly different underlying behaviors. In addition, within each phase, normal processes in general follow certain underlying operation rules, called inner-phase evolution here, which however have not been addressed before. In this paper, a new statistical modeling and online monitoring method is proposed for multiphase batch processes. A two-level phase division algorithm is proposed to capture the process trend and trace inner-phase evolutions. It reveals that the inner-phase process in general goes through three statuses sequentially, i.e., transition, steady phase, and transition. Principal component analysis (PCA) and qualitative trend analysis (QTA) are combined to distinguish different inner-phase process statuses. Their different characteristics are then modeled and monitored separately, revealing more accurate process operation information. Meanwhile, the problem of uneven-duration batches is effectively handled in different inner-phase process statuses. The application to a typical multiphase batch process, injection molding, illustrates the feasibility and performance of the proposed algorithm.

show abstract

Section: Model Development For Transitionsmentioning

confidence: 94%

Inner-Phase Analysis Based Statistical Modeling and Online Monitoring for Uneven Multiphase Batch Processes

Zhao

Gao

2013

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For unevenlength batches, they can be synchronized using different methods, [23][24][25][26][27] such as dynamic time warping 23 , instrumental variable, 24 and multisynchro. 25,26 Then, observations with the same time stamp are comparable and can be used for model development. The specific process time can be used to indicate which representative model should be employed for process monitoring.…”

Section: An Itspp Methodsmentioning

confidence: 99%

An iterative two‐step sequential phase partition (ITSPP) method for batch process modeling and online monitoring

2016

Self Cite

View full text Add to dashboard Cite

in Wiley Online Library (wileyonlinelibrary.com) 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.

show abstract

“…There are four main procedures in injection molding process: injection, packing-holding, plastication and cooling [18]. Experiments are conducted using high-density polyethylene (HDPE) as the feed stocks [10]. Main process variables are collected online, and final qualities are obtained at the end of each batch.…”

Section: A Injection Moldingmentioning

confidence: 99%

“…works [6][7][8][9][10] have been done focusing on multi-phase characteristic of batch processes. Different MPCA models were established to monitor different phases with the estimation of process data [6].…”

mentioning

confidence: 99%

“…Considering the transition problem between neighboring phases, a soft-transition multiple PCA (STMPCA) method was proposed to model and detect transitions, and further to monitor the whole process [8,9]. Considering that there are both similarity and dissimilarity among different groups of uneven-duration batches, Zhao et al proposed to model group-common and specific information respectively to handle uneven-duration problem [10].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Inner-phase-evolution-traced statistical modeling and online monitoring for uneven batch processes

Zhao

Gao

2013

2013 10th IEEE International Conference on Control and Automation (ICCA)

View full text Add to dashboard Cite

Most batch processes have multiple phases with different characteristics. Within each phase, processes usually evolve following certain underlying rules, called inner-phase evolution here. For normal processes, these evolution rules must be obeyed, and any violation of the inner-phase evolutions is deemed to be abnormal. In this paper, a new statistical modeling and online monitoring method is proposed by combining principal component analysis (PCA) and qualitative trend analysis (QTA) to trace inner-phase evolutions of batch processes. By this method, inner-phase evolutions are traced, offering more information about whether the process is operating under normal status. Meanwhile, the problem of uneven-duration batches can be handled. Transitions are also modeled and monitored effectively. A chart showing the evolutions of variable contributions to a fault is designed for fault diagnosis. This method is applied to an injection molding process, revealing satisfactory monitoring and fault detection results.

show abstract

Statistical analysis and online monitoring for handling multiphase batch processes with varying durations

Cited by 51 publications

References 29 publications

Inner-Phase Analysis Based Statistical Modeling and Online Monitoring for Uneven Multiphase Batch Processes

Inner-Phase Analysis Based Statistical Modeling and Online Monitoring for Uneven Multiphase Batch Processes

An iterative two‐step sequential phase partition (ITSPP) method for batch process modeling and online monitoring

Inner-phase-evolution-traced statistical modeling and online monitoring for uneven batch processes

Contact Info

Product

Resources

About