On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis.

Lee, Jongmin; Lee, In‐Beum

doi:10.1252/jcej.36.1384

Cited by 31 publications

(24 citation statements)

References 44 publications

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“…Lee et al [49] and Yoo et al [93] respectively generated 50 normal and 1 faulty, and 60 normal and 2 faulty Pensim batches to test SPM via Multi-way Independent Component Analysis (MICA). [3] conducted a more extensive test of MICA using Pensim (15 normal, 2 faulty) and DuPont datasets, and a third set of 40 normal runs and 1 faulty run of a simulated semi-batch production of polyol lubricant [96].…”

Section: Introductionmentioning

confidence: 99%

An extensive reference dataset for fault detection and identification in batch processes

Impe¹,

Gins²

2015

Chemometrics and Intelligent Laboratory Systems

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Close process monitoring (i.e., detection and identification of disturbances) is important to achieve high process efficiency and safety. The Tennessee Eastman process is an extensive benchmark dataset for fault detection and identification, but it is only representative for continuous processes because it does not contain the inherent nonstationarity that complicates monitoring of batch processes. Nevertheless, batch processes also play an important role in many types of industry. This paper therefore presents an extensive reference dataset for benchmarking data-driven methodologies for fault detection and identification in batch processes. The original Pensim model [10] is expanded with sensor noise. By changing the properties of the initial conditions and/or model parameters, four subsets of different complexity are generated, each containing 400 batches with normal operation. To correctly assess the fault detection and identification in batch processes, 15 faults are simulated with various amplitudes and onset times for a total of 22,200 faulty batches for each subset, or 90,400 batches in total. Analysis of the data indicates that the presented types of process faults and their various amplitudes in each of the four subsets present a suitable benchmark for fault detection and identification in batch processes. The dataset is freely available at http://cit.kuleuven.be/biotec/batchbenchmark.

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Section: Introductionmentioning

confidence: 99%

An extensive reference dataset for fault detection and identification in batch processes

Impe¹,

Gins²

2015

Chemometrics and Intelligent Laboratory Systems

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“…In other words, ICA is seeking a set of independent components from the measured process variables. Therefore, it is quite natural to infer that monitoring based on the ICA solution may give better results compared with monitoring the original data (Lee et al 2003a) because the original data could be a mixture of noise and process characteristics such as process disturbances and/or autocorrelation .…”

Section: No Training Phasementioning

confidence: 99%

An independent component analysis-based disturbance separation scheme for statistical process monitoring

2010

J Intell Manuf

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In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.

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“…In his work, a set of devised SPC charts have been developed effectively for each IC. Lee et al [26,27] investigated the utilization of kernel density estimation to define the control limits of the ICs that do not satisfy Guassian distribution. To monitor batch processes that combine ICA and kernel estimation, Lee et al [25] extended their original method to multi-way ICA.…”

Section: Spatiotemporal Ica In Process Controlmentioning

confidence: 99%

Process disturbance identification through integration of spatiotemporal ICA and CART approach

Chiu

Hwang

Cook

et al. 2009

Neural Comput & Applic

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Many studies have been conducted over the past several years evaluating the integrated use of statistical process control (SPC) and engineering process control (EPC). The majority of these studies reported that combining SPC with EPC outperforms the use of only SPC or EPC. Basically, the former aims to rapidly detect assignable causes and time points for abnormalities that take place during process; and the latter is a method in which input variables are adjusted against process outputs through a feedback control mechanism. Although combining SPC with EPC can effectively detect time points when abnormalities occur during process, their combination can also cause an increased occurrence of false alarms when autocorrelation is present in the process. In this study, to increase the accuracy of process disturbance identification, we propose the integration of spatiotemporal independent component analysis (stICA) with the classification and regression tree (CART) approach to improve our capability to identify process disturbances and recognize shifts in the correlated process parameters. The integration of the CART methodology results in the development of decision rules that can provide valuable information related to the impact of variation in process variable values. These decision rules can provide an increased understanding of process behavior and useful information for process control. For comparison, the integration of traditional principle component analysis (PCA) with CART (called PCA-CART), ICA with CART (called ICA-CART) and cumulative sum chart approaches were applied to evaluate the identification capability of the proposed approach. As the results reveal, the proposed approach is more effective for monitoring correlated process.

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On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis.

Cited by 31 publications

References 44 publications

An extensive reference dataset for fault detection and identification in batch processes

An extensive reference dataset for fault detection and identification in batch processes

An independent component analysis-based disturbance separation scheme for statistical process monitoring

Process disturbance identification through integration of spatiotemporal ICA and CART approach

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