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Design multivariate statistical process control procedure in the case of Ethio cement

INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2022年第7期39卷 1617-1636页

作者： Tegegne, Daniel Ashagrie Azene, Daniel Kitaw Atanaw, Eshetie Berhan Addis Ababa Inst Technol Mech & Ind Engn Addis Ababa Ethiopia

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T-2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production ***/methodology/approachmultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T-2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control *** finding of this paper is that the points and lines in the classic Hotelling T-2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T-2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural ***/valueModifying the classical Hotelling T-2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

关键词： multivariate statistical process control Hotelling T-2 Neural network Graph theory Clinker production process control GANNT chart

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Drowsiness Detection Using multivariate statistical process control 22nd

Drowsiness Detection Using Multivariate Statistical Process ...

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22nd International Conference on Computational Science and its Applications (ICCSA)

作者： Antunes, Ana Rita Braga, Ana Cristina Goncalves, Joaquim Polytech Inst Cavado & Ave 2Ai P-4750810 Barcelos Portugal Univ Minho ALGORITMI Ctr P-4710057 Braga Portugal

ISBN: (纸本)9783031105364;9783031105357

Drowsiness at the wheel has been studied for different countries since it is important for road safety and its prevention. Since it is considered a public health problem, solutions must be found to avoid worse scenarios and to identify a low-cost system. Therefore, this work aims to detect the drowsy state, without labeling it manually, considering the heart rate variability. To make this possible, driving simulations were performed, using a wearable device. In terms of methodology, multivariate statistical process control, considering principal component analysis, was implemented, and compared with a similar study. Three principal components were computed taking into consideration time, frequency, and non-linear domain, every two minutes. Thereafter, Hotelling T-2 and squared prediction error statistics were estimated. These statistics were estimated considering each principal component, individually. Thereby, the results achieved seemed to be promising to identify drowsiness peaks. However, the study developed has limitations, like the identification of points out-of-control occurred due to signal noise and it does not identify all the drowsiness peaks. Conversely, it was not used information from the participants' awake states as a reference. Therewith, new simulations must be done, and new information must be added to avoid noise and to detect more drowsiness peaks.

关键词： Drowsy Driving Simulation Heart rate variability multivariate statistical process control Principal component analysis

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Random sampling strategies for multivariate statistical process control to detect cyber-physical manufacturing attacks

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QUALITY ENGINEERING 2021年第2期33卷 300-317页

作者： Elhabashy, Ahmad E. Dastoorian, Romina Wells, Lee J. Camelio, Jaime A. Alexandria Univ Prod Engn Dept Fac Engn Alexandria Egypt Western Michigan Univ Dept Ind & Entrepreneurial Engn & Engn Management Kalamazoo MO USA Univ Georgia Coll Engn Athens GA 30602 USA

With the latest advances in computer and networking technologies, the threat of cyber-physical attacks against manufacturing systems is growing. Unlike traditional cyber-attacks, cyber-physical attacks are not limited to intellectual property theft and affect the physical world, which could be devastating to manufacturing, if they are undetected. Relying on traditional quality control to defend against these malicious attacks, manufacturers can choose to either closely monitor a large number of potential quality characteristics or only monitor a specific subset of the characteristics. However, the former choice may be impractical when a large number of potential characteristics exists, whereas the latter might be susceptible to an intelligently designed attack that targets unmonitored characteristics. Therefore, a novel random variable-selection approach that is both resilient to malicious cyber-physical attacks and sensitive to shifts over a small subset of characteristics is proposed in this work. Such an approach is based upon random sampling strategies when using multivariate Hotelling T (2) control charts. To assess its usefulness, the proposed approach was compared to an established variable-selection method, using a simplified cost model. The obtained results show that the proposed approach is both cost-effective and well-suited for industrial applications where the number of quality characteristics to monitor is quite significant.

关键词： cyber-physical security manufacturing systems multivariate statistical process control quality control random sampling

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

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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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Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2020年第9期412卷 2151-2163页

作者： de Oliveira, Rodrigo R. Avila, Claudio Bourne, Richard Muller, Frans de Juan, Anna Univ Barcelona Dept Analyt Chem Chemometr Grp Diagonal 645 E-08028 Barcelona Spain Univ Leeds Sch Chem & Proc Engn Leeds LS2 9JT W Yorkshire England

process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs.

关键词： Data fusion multivariate statistical process control Near-infrared Spectroscopic sensors Chemometrics

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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 statistical process control in annual pharmaceutical product review

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JOURNAL OF process control 2018年 69卷 97-102页

作者： Torres, Adamastor R. de Oliveira, Antonio Diogenes P. Grangeiro Junior, Severino Fragoso, Wallace D. Univ Fed Paraiba Dept Chem Joao Pessoa Paraiba Brazil Governor Miguel Arraes LAFEPE Pharmaceut Lab State Pernambuco Recife PE Brazil

Annual Product Review recommending by the Food and Drug Administration and the International Conference on Harmonization is a periodic assessment of products quality to verifying the consistency of the production process and the quality of the finished product. In this work it was performed an annual product review using multivariate statistical process control. multivariate control charts was used for the periodic evaluation of the Hydrochlorothiazide 25 mg tablet, produced between 2009 and 2013 in the Pharmaceutical Laboratory of Pernambuco (Brazil). The procedure has allowed one to monitor and diagnose deviations in the production process. A corrective action was taken in the process by the Quality System in 2011 and the methodology applied was used to evaluate this action's effectiveness. Through the multivariate statistical process control it was possible to make an annual product review in accordance with the pharmaceutical industry regulatory requirements. (C) 2018 Published by Elsevier Ltd.

关键词： Annual product review multivariate statistical process control Principal component analysis Reconstruction-based contributions Hydrochlorothiazide

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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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A graphical user interface for PCA-based MSPC: A benchmark software for multivariate statistical process control in MATLAB

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 185卷 135-152页

作者： Villalba, Pedro Sanchis, Javier Ferrer, Alberto Univ Politecn Valencia Dept Estadist & Invest Operat Aplicadas & Calidad Camino Vera S-N E-46022 Valencia Spain Univ Politecn Valencia Inst Univ Automat & Informat Ind Camino Vera S-N E-46022 Valencia Spain

A Graphical User Interface (GUI) is developed in MATLAB as a tutorial for understanding the PCA-based MSPC strategy. The software allows users to analyze both simulated and external data sets. Simulated data are obtained from a nonlinear model of a binary distillation column implemented in Simulink. The nonlinear model has four manipulated variables, four controlled variables and three input measured disturbances, plus 41 M fractions corresponding to every column stage. The methodology for PCA-based MSPC is implemented in two phases. During Phase I, the user can simulate the distillation column under normal operating conditions at three different operating points. When the simulation is finished, the GUI obtains the corresponding PCA model automatically. In Phase II, the user can simulate several scenarios with different combinations of disturbances and failures and monitor them using Squared Prediction Error (SPE) and T-2 control charts. Contribution plots are used to diagnose the original variables responsible of such abnormal situations. The software also incorporates the possibility to analyze external multivariate process datasets.

关键词： multivariate statistical process control Principal component analysis Latent variable multivariate control charts Contribution plots Benchmark Nonlinear distillation column Tutorial GUI

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Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using multivariate statistical process control

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IRBM 2019年第6期40卷 320-331页

作者： Rukhsar, S. Khan, Y. U. Farooq, O. Sarfraz, M. Khan, A. T. Indian Inst Technol Dept Elect Engn Jodhpur 342037 Rajasthan India Aligarh Muslim Univ Fac Engn & Technol Dept Elect Engn Aligarh 202002 Uttar Pradesh India Aligarh Muslim Univ Fac Engn & Technol Dept Elect Engn Aligarh 202002 Uttar Pradesh India

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed. Result: The results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour. Conclusion: This study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power. Significance: The proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base. (C) 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.

关键词： multivariate statistical process control Squared prediction error (Q) T-2 Hotelling parameters PCA Epilepsy prediction

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