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Anomaly Detection of Continuous Wet Granulation using multivariate statistical process control (MSPC)

KAGAKU KOGAKU RONBUNSHU 2022年第3期48卷 99-103页

作者： Oishi, Takuya Nagato, Takuya Minamiguchi, Takuya Kim, Sanghong Powrex Corp Div Res & Dev 5-5-5 Kita Gawara Itami Hyogo 6640837 Japan Kyoto Univ Dept Chem Engn Nishikyo Ku Kyoto 6158246 Japan Tokyo Univ Agr & Technol Dept Appl Phys & Chem Engn 2-24-16 Naka Cho Koganei Tokyo 1848588 Japan

The multivariate statistical process control (MSPC) was applied as an anomaly detection method in the continuous manufacturing of drugs. It was confirmed whether abnormal changes in process parameters which were intentionally introduced during operation of a continuous wet granulator could be detected by MSPC. As abnormalities, the water addition rate, center blade rotation speed, and particle sizes of raw materials were varied, whereby all of the abnormalities could be detected with high precision by monitoring of statistics using MSPC. Moreover, it was possible to correctly confirm the normal operating state as being the normal operating.

关键词： multivariate statistical process control Continuous Manufacturing Pharmaceutical Production Continuous Wet Granulation

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A Monitoring Technique Using multivariate statistical process control Method for Performance Improvement with Application to Wastewater Treatment Plant Operation

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IFAC Proceedings Volumes 2012年第16期45卷 619-624页

作者： O. Yamanaka N. Yoshizawa Y. Hiraoka K. Sano T. Hamada Toshiba Corporation Tokyo Japan Japan Sewage Works Agency Tokyo Japan

This paper presents a new monitoring technique to improve the performance of plant operation based on multivariate statistical process control (MSPC). The proposed method combines with MSPC by principal component analysis (PCA-MSPC) and monitoring of a predefined performance index for efficient and stable plant operation. Fault detection and isolation (FDI) related to the performance index is selectively performed by monitoring the time series data of the index wherein the sample points violating the control limit of the Q statistic or that of the T 2 statistic in PCA-MSPC are indicated. Hidden patterns of probable cause to deteriorate the performance index are discovered from the FDI by observing the time series data of the isolated variables. Applications of the proposed method to real wastewater treatment process illustrate the effectiveness of the proposed method by showing possible improvement for energy-saving operation and stable plant operation.

关键词： multivariate statistical process control Monitoring and Fault Detection Energy Saving, Performance Improvement Wastewater Treatment process Principal Component Analysis

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Development of Drowsiness Detection Method by Integrating Heart Rate Variability Analysis and multivariate statistical process control

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SICE Journal of control, Measurement, and System Integration 2016年第1期9卷 10-17页

作者： Erika Abe Koichi Fujiwara Toshihiro Hiraoka Toshitaka Yamakawa Manabu Kano Department of Systems Science Kyoto University Priority Organization for Innovation and Excellence also with the Department of Computer Science and Electrical Engineering Kumamoto University

Drowsy driving accidents can be prevented if predicted in advance. The present work aims to develop a new method for detecting driver drowsiness based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is a fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses eight HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in the field of process control. In the proposed method, only one principal component was adopted in MSPC and driver drowsiness was detected through monitoring the T2 statistic. Driving simulator experiments demonstrated that driver drowsiness was successfully detected in seven out of eight cases before accidents occurred. In addition, the proposed method was implemented in a smartphone app for on-vehicle use.

关键词： drowsiness detection heart rate variability analysis multivariate statistical process control smartphone app

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Unsupervised Fault Detection for Refrigeration Showcase Systems with Kernel Principal Component Analysis based multivariate statistical process control using Feature Selection with Maximal Information Coefficient

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IEEJ JOURNAL OF INDUSTRY APPLICATIONS 2021年第5期10卷 598-605页

作者： Arai, Kiyo Fukuyama, Yoshikazu Murakami, Kenya Matsui, Tetsuro Meiji Univ Grad Sch Adv Math Sci Nakano Ku 4-21-1 Nakano Tokyo 1648525 Japan Fuji Elect Co Ltd AI Solut Ctr Digital Innovat Lab Corp R&D Headquarters Fujimachi Hino Tokyo 1918502 Japan

This paper proposes a kernel principal component analysis (KPCA) based multivariate statistical process control (KPCA-MSPC) method for fault detection of refrigeration showcase systems using a feature selection method with maximal information coefficient (MIC). Refrigeration showcase system data include non-linear relationships among pairs of features, and only normal data can be available for training generally. KPCA-MSPC is suitable for the fault detection because it is an unsupervised method and can handle non-linear relationships. In showcase systems, a large number of measured data can be obtained and they can be utilized as features for fault detection. However, considering system costs, the number of sensors installed in the showcase systems and the amount of data stored in data centers are limited. Therefore, a feature selection method based on MIC and k-nearest neighbor algorithm (KNN) (MIC-KNN-FS) suitable for KPCA-MSPC is proposed. The effectiveness of the combination of KPCA-MSPC and the proposed MIC-KNN-FS for showcase systems is verified by comparison with the Laplacian Score feature selection method (LS-FS) and the KNN feature selection method (KNN-FS), which are typically utilized as feature selection methods, and cumulative autoencoders (CAE) and MSPC based on PCA (PCA-MSPC), which are unsupervised fault detection methods.

关键词： showcase system fault detection feature selection kernel principal component analysis multivariate statistical process control maximal information coefficient

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multivariate nonlinear statistical process control of a sequencing batch reactor

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2006年第1期39卷 43-51页

作者： Yoo, CK Villez, K Lee, IB Vanrolleghem, PA Univ Ghent BIOMATH Dept Appl Math B-9000 Ghent Belgium Pohang Univ Sci & Technol Dept Chem Engn Sch Environm Sci & Engn Pohang 790784 South Korea

This research describes the application of a multivariate statistical process control method to a pilot-scale sequencing batch reactor (SBR) using a hatchwise nonlinear monitoring technique for a denoising effect. Three-way batch data of normal batches are unfolded batch-wise and then a kernel principal component analysis (KPCA) is applied to capture the nonlinear dynamics within normal batch processes. The developed monitoring method was successfully applied to an 80-l sequencing batch reactor (SBR) for biological wastewater treatment, which is characterized by a variety of nonstationary and nonlinear characteristics. In the multivariate analysis and batch-wise monitoring, the developed nonlinear monitoring method can effectively capture the nonlinear relations within the batch process data and clearly showed the power of nonlinear process monitoring and denoising performance in comparison with linear methods.

关键词： batch monitoring bioprocess kernel principal component analysis multivariate statistical process control nonlinear multivariate analysis and monitoring sequencing batch reactor (SBR) wastewater treatment process (WWTP)

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Dynamic multivariate statistical process control using partial least squares and canonical variate analysis

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COMPUTERS & CHEMICAL ENGINEERING 1999年第Sup期23卷 S277-S280页

作者： Simoglou, A Martin, EB Morris, AJ Univ Newcastle Upon Tyne CPACT Foresight Ctr Proc Analyt & Control Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England

multivariate statistical techniques have been shown to be useful tools for multivariate statistical process control (MSPC) and process modelling. However, these approaches have been mainly applied to static systems. In the present work, a well known system representation, the state space model, is developed to deal with dynamic situations. The states of the system are approximated using two multivariate statistical projection techniques, Partial Least Squares (PLS) and Canonical Variate Analysis (CVA). These two model representations are compared both in terms of their predictive ability and also their monitoring power using a simulation example. An application to an industrial fluidised bed reactor will be presented at the conference following company approval.

关键词： multivariate statistical process control state space models partial least squares canonical variate analysis statistical process monitoring prediction of product quality

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On the Feasibility of Distinguishing Between process Disturbances and Intrusions in process control Systems using multivariate statistical process control

On the Feasibility of Distinguishing Between Process Disturb...

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Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops

作者： Mikel Iturbe Jose Camacho Inaki Garitano Urko Zurutuza Roberto Uribeetxeberria Department of Electronics and Computing Faculty of Engineering Mondragon University Department of Signal Theory Telematics and Communications - CITIC University of Granada

ISBN: (纸本)9781509036899

process control Systems (PCSs) are the operating core of Critical Infrastructures (CIs). As such, anomaly detection has been an active research field to ensure CI normal operation. Previous approaches have leveraged network level data for anomaly detection, or have disregarded the existence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In this paper we present an anomaly detection and diagnostic system based on multivariate statistical process control (MSPC), that aims to distinguish between attacks and disturbances. For this end, we expand traditional MSPC to monitor process level and controller level data. We evaluate our approach using the Tennessee-Eastman process. Results show that our approach can be used to distinguish disturbances from intrusions to a certain extent and we conclude that the proposed approach can be extended with other sources of data for improving results.

关键词： process control systems multivariate statistical process control Tennessee-Eastman process control system statistical process control anomaly detection process control Data sources intrusions disturbances

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Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet metal forming process 55

Deep learning for multivariate statistical in-process contro...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Biegel, Tobias Jourdan, Nicolas Hernandez, Carlos Cviko, Amir Metternich, Joachim Otto-Berndt-Str. 2 Darmstadt64287 Germany Blumenstraße 5 Köngen 73257 Germany

Detecting abnormal conditions in manufacturing processes is a crucial task to avoid unplanned downtimes and prevent quality issues. The increasing amount of available high-frequency process data combined with advances in the field of deep learning offers huge potential in enhancing the performance of existing multivariate statistical process control approaches. We investigate the application of deep autoencoder-based monitoring approaches and experiment with the reconstruction error and the latent representation of the input data to compute Hotelling's T2 and Squared Prediction Error monitoring statistics. The investigated approaches are validated using a real-world sheet metal forming process and show promising results. © 2022 The Authors. Published by Elsevier B.V.

关键词： Anomaly Detection Autoencoder Deep Learning Discrete Manufacturing multivariate statistical process control

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Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet metal forming process

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Procedia CIRP 2022年 107卷 422-427页

作者： Tobias Biegel Nicolas Jourdan Carlos Hernandez Amir Cviko Joachim Metternich Institute for Production Management Technology and Machine Tools (PTW) Otto-Berndt-Str. 2 64287 Darmstadt Germany Staufen. Quality Engineers GmbH (SQE) Blumenstraße 5 73257 Köngen Germany

Detecting abnormal conditions in manufacturing processes is a crucial task to avoid unplanned downtimes and prevent quality issues. The increasing amount of available high-frequency process data combined with advances in the field of deep autoencoder-based monitoring offers huge potential in enhancing the performance of existing multivariate statistical process control approaches. We investigate the application of deep auto encoder-based monitoring approaches and experiment with the reconstruction error and the latent representation of the input data to compute Hotelling’s T 2 and Squared Prediction Error monitoring statistics. The investigated approaches are validated using a real-world sheet metal forming process and show promising results.

关键词： Deep Learning Autoencoder multivariate statistical process control Discrete Manufacturing Anomaly Detection

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A multivariate change-point model for statistical process control

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TECHNOMETRICS 2006年第4期48卷 539-549页

作者： Zamba, K. D. Hawkins, Douglas M. Univ Iowa Coll Publ Hlth Dept Biostat Iowa City IA 52242 USA Univ Minnesota Sch Stat Minneapolis MN 55455 USA

multivariate statistical process control (SPC) carries out ongoing checks to ensure that a process is in control. These checks on the process are traditionally done by T-2, multivariate cusum, and multivariate exponentially weighted moving average control charts. These traditional SPC charts assume that the in-control true parameters are known exactly and use these assumed true values to set the control limits. The reality, however, is that true parameter values are seldom if ever known exactly;rather, they are commonly estimated from a Phase I sample. It is increasingly recognized that this Phase I study needs to involve large samples if the parameter estimates are to provide run behavior matching that of the known-parameter situation. But, apart from the general undesirability of large and thus expensive studies preliminary to actual charting, some industrial settings have a paucity of relevant data for estimating the process parameters. An attractive alternative to traditional charting methods when monitoring for a step change in the mean vector is an unknown-parameter likelihood ratio test for a change in mean of p-variate normal data. We have found that this approach description is able to control the run behavior despite the lack of a large Phase I sample.

关键词： average run length change point likelihood ratio multivariate statistical process control phases I and II T-2 statistic

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