ZAGO, DANIELE · Research Padua Archive (University of Padua)
In recent years, significant advancements have been made in the field of statistical process monitoring. Traditional control charts typically rely on the normality assumption, which is often not satisfied in practical applications. As data complexity increases, many control charts introduced in contemporary literature can only be effectively analyzed through simulation methods due to their analytical or numerical intractability. When control charts are calibrated using simulation-based methods, they can be used in a wider variety of contexts compared to traditional methodologies. This transition towards simulation-based methodologies introduces several practical challenges that warrant careful analysis. The ongoing development of more efficient algorithms and solutions is essential from an applied perspective. This thesis contributes to the field of statistical process monitoring by presenting novel computational methodologies and software solutions. These are designed to enhance the applicability of control charts in scenarios where simulation techniques are preferred over conventional numerical methods. The thesis is organized as follows: the first chapter comprises a software solution that provides general tools for practical statistical process monitoring; the second chapter introduces a novel approach for monitoring mixed data, which is increasingly relevant in practice; third and fourth chapters present two algorithmic contributions focused on enhancing the efficiency of control limit calibration and hyperparameter tuning for control charts, respectively.
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Knoth, Sven, Okhrin, Yarema, Otto, Philipp · 2024
This book presents a unique collection of contributions on modern methods and applications in three key areas of statistics, celebrating the significant work of Wolfgang Schmid in this field. It is structured thematically into parts focusing on statistical process monitoring, financial statistics, and spatial statistics with environmetrics, each featuring chapters from leading experts. The opening articles on statistical process monitoring present novel methodologies for the detection of anomalies and control charting techniques, which are crucial for maintaining quality in manufacturing processes. Detailed discussions are included on integrating multivariate statistical methods and real-time monitoring to enhance process reliability and efficiency. The part on financial statistics explores rigorous approaches in financial econometrics, with an emphasis on dynamic modelling of market volatility and risk assessment. Contributions cover advanced asset allocation strategies, leveraging high-dimensional data analysis, and the application of machine learning techniques. Spatial statistics and environmetrics are addressed through innovative research on the statistical analysis of environmental data. This includes the use of geostatistical models and hybrid models that combine traditional statistical techniques with machine learning to improve the prediction of environmental phenomena. Key topics here involve the modelling of extremes and airborne pollutants, the prediction of earthquakes using a smartphone-based sensor network, and reviews of selected topics essential in modern spatial statistics. Each part not only reflects Wolfgang Schmid’s interests and impact in these areas but also provides detailed theoretical and applied studies, demonstrating how these sophisticated statistical methods can be effectively employed in practical scenarios. This makes the book an indispensable resource for researchers and practitioners looking to apply cutting-edge statistical techniques in these complex fields.
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Harrou, Fouzi, Sun, Ying, Hering, Amanda et al. · Elsevier eBooks · 2020
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Knoth, Sven, Okhrin, Yarema 1978-, Otto, Philipp 1989- et al.
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ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · 2024
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Konstantinos A. Tasias · National Archive of Doctoral Theses (National Documentation Center (Greece)) · 2016
Τα πλήρως δυναμικά διαγράμματα ελέγχου είναι άκρως αποτελεσματικά εργαλεία Στατιστικού Ελέγχου Διεργασιών για την παρακολούθηση ενός χαρακτηριστικού ποιότητας το οποίο επηρεάζει το αποτέλεσμα μιας παραγωγικής διαδικασίας. Συνήθως, η λειτουργία των διαγραμμάτων ελέγχου εξετάζεται σε διαδικασίες οι οποίες επηρεάζονται από μία συστηματική αιτία. Ωστόσο, αυτή η υπόθεση δεν είναι ρεαλιστική καθώς η μεταβολή της διαδικασίας σε κατάσταση εκτός στατιστικού ελέγχου μπορεί να αποτελεί το αποτέλεσμα πολλών συστηματικών αιτιών, οι οποίες είναι πιθανό να συμβούν ανεξάρτητα και μπορεί να επιδρούν στη μέση τιμή της διαδικασίας, στην τυπική απόκλιση αυτής ή και στα δύο. Επιπλέον, η προφανής συσχέτιση μεταξύ των πεδίων του ποιοτικού ελέγχου και της συντήρησης εξοπλισμού και η βελτίωση της κατάστασης της διαδικασίας σε περίπτωση εκτέλεσης προληπτικών ενεργειών κάνει απαραίτητη τη μελέτη αυτής της συσχέτισης σε περιπτώσεις πολλαπλών συστηματικών αιτιών. Ένα ακόμη σύνθετο πρόβλημα το οποίο συναντάται συχνά σε πλήθος σύγχρονων εφαρμογών είναι η ταυτόχρονη παρακολούθηση πολλαπλών χαρακτηριστικών ποιότητας, αλληλοεξαρτημένων μεταξύ τους. Είναι προφανές ότι η παρακολούθηση κάθε χαρακτηριστικού ποιότητας ξεχωριστά οδηγεί σε λανθασμένες αποφάσεις, ειδικά σε περιπτώσεις έντονης συσχέτισης μεταξύ αυτών των χαρακτηριστικών. Στην παρούσα διατριβή, αναπτύχθηκαν πλήρως δυναμικά διαγράμματα ελέγχου τύπου Shewhart για την ταυτόχρονη παρακολούθησης της θέσης και της διασποράς διαδικασιών στις οποίες είναι πιθανό να επιδράσουν πολλαπλές συστηματικές αιτίες. Όλα τα διαγράμματα ελέγχου είναι οικονομικά και στατιστικά βελτιστοποιημένα. Επίσης, για κάθε προτεινόμενο διάγραμμα ελέγχου έχει αναπτυχθεί αντίστοιχη Μαρκοβιανή Αλυσίδα Διακριτού χρόνου η οποία μοντελοποιεί την πιθανή επίδραση πολλαπλών αιτιών που οδηγούν σε βαθμιαία υποβάθμιση της διαδικασίας. To πρώτο προτεινόμενο διάγραμμα ελέγχου είναι ένα νέο οικονομικο-στατιστικά βελτιστοποιημένο, πλήρως δυναμικό σχήμα ελέγχου για την παρακολούθηση διαδικασιών στις οποίες είναι πιθανό να επιδράσουν δύο συστηματικές αιτίες, μία εκ των οποίων επιδρά στη μέση τιμή και μία στην τυπική απόκλιση της διαδικασίας. Συνεπώς, η διαδικασία είναι πιθανό να λειτουργεί σε μία από τις ακόλουθες τέσσερις καταστάσεις: σε στατιστικό έλεγχο, όταν καμία συστηματική αιτία δεν έχει συμβεί, υπό την επίδραση της μίας εκ των δύο συστηματικών αιτιών ή υπό την επίδραση και των δύο αιτιών. Επίσης, πραγματοποιήθηκε σύγκριση του συγκεκριμένου πλήρως δυναμικού μοντέλου με το αντίστοιχο στατικό και με λιγότερο δυναμικά διαγράμματα για πλήθος διαφορετικών περιπτώσεων, η οποία ανέδειξε την οικονομική και στατιστική ανωτερότητά του έναντι των υπολοίπων. Επιπρόσθετα, παρουσιάζεται ο οικονομικο-στατιστικός σχεδιασμός ενός πλήρως δυναμικού διαγράμματος ελέγχου για την παρακολούθηση της θέσης διαδικασιών που υπόκεινται σε πιθανή επίδραση πολλαπλών συστηματικών αιτιών που επηρεάζουν τη μέση τιμή. Και σε αυτή την περίπτωση η σύγκριση του μοντέλου σε σχέση με το αντίστοιχο στατικό και τα λιγότερο δυναμικά διαγράμματα ανέδειξε καλύτερα αποτελέσματα σε πληθώρα περιπτώσεων. Στο πλαίσιο της παρούσας διατριβής εξετάστηκε το πρόβλημα της πιθανής επίδρασης πολλών συστηματικών αιτιών που επηρεάζουν και τη θέση και τη διασπορά του χαρακτηριστικού ποιότητας και αναπτύχθηκε πλήρως δυναμικό σχήμα ελέγχου για την παρακολούθηση αυτών των διαδικασιών. Οι συστηματικές αιτίες είναι πιθανό να οδηγήσουν σε βαθμιαία υποβάθμιση της κατάστασης της διαδικασίας ενώ οι υποθέσεις για πιθανή ταυτόχρονη επίδραση παραπάνω της μίας συστηματικής αιτίας και διαφορετικών διορθωτικών ενεργειών για καθεμία εξ αυτών κάνει το προτεινόμενο μοντέλο άκρως ρεαλιστικό. Μέσω μιας εκτεταμένης αριθμητικής ανάλυσης συμπεραίνεται η οικονομική και στατιστική ανωτερότητα του συγκεκριμένου σχήματος ελέγχου έναντι απλούστερων προσεγγίσεων, ενώ μια εφαρμογή από την αεροπορική βιομηχανία προβάλλει την ευρεία εφαρμοστικότητα του. Ακόμη, ένα νέο πλήρως δυναμικό διάγραμμα αναπτύχθηκε στην παρούσα διατριβή για την παρακολούθηση διαδικασιών όπου πέραν των πολλαπλών συστηματικών αιτιών θέσης και διασποράς είναι πιθανό να επιδράσουν στη διαδικασία και βλάβες. Κάθε φορά που το διάγραμμα ελέγχου υποδεικνύει πιθανή εκτός ελέγχου λειτουργία ενεργοποιούνται διαδικασίες προληπτικής συντήρησης οι οποίες είναι προτιμητέες έναντι των εργασιών διορθωτικής συντήρησης που απαιτούνται μετά από την επίδραση βλάβης. Ο βέλτιστος σχεδιασμός του συγκεκριμένου διαγράμματος πραγματοποιείται μέσω της επίλυσης ενός προβλήματος βελτιστοποίησης δύο αντικειμενικών συναρτήσεων: της ελαχιστοποίησης του συνολικού κόστους ανά μονάδα χρόνου και της μεγιστοποίησης της διαθεσιμότητας του εξοπλισμού. Μία εκτεταμένη αριθμητική ανάλυση χρησιμοποιείται για την ανάδειξη της ανωτερότητας του προτεινόμενου μοντέλου σε σχέση με τα αντίστοιχα απλούστερα διαγράμματα.Ένα πλήρως δυναμικό διάγραμμα ελέγχου παρακολούθησης διαδικασιών οι οποίες χαρακτηρίζονται από πολλαπλά χαρακτηριστικά ποιότητας και υπόκεινται σε πολλαπλές συστηματικές αιτίες θέσης και διασποράς μελετήθηκε στο πλαίσιο της διατριβής. Οι συστηματικές αιτίες είναι ανεξάρτητες μεταξύ τους και είναι πιθανό να επηρεάσουν και τον πίνακα μέσων τιμών και τον πίνακα διακύμανσης/συνδιακύμανσης, οι οποίοι παρακολουθούνται μέσω ενός διαγράμματος Τ2 και ενός διαγράμματος τύπου Shewhart βασισμένου στο μέγεθος της εντροπίας, αντίστοιχα. Για την παρουσίαση της λειτουργίας του συγκεκριμένου σχήματος ελέγχου και λόγω της πολυπλοκότητας αυτού αναλύθηκε η εφαρμογή του σε ένα πραγματικό παράδειγμα.Συμπερασματικά, τα κυριότερα συμπεράσματα της διατριβής συνοψίζονται στα ακόλουθα: • Αναπτύχθηκαν πρωτότυπα, εύκολα στη χρήση, εργαλεία ελέγχου μέσω των οποίων είναι εφικτή η παρακολούθηση διαδικασιών οι οποίες υπόκεινται σε πιθανή επίδραση πληθώρας συστηματικών αιτιών θέσης και διασποράς. Επίσης, είναι εφικτή η παρακολούθηση διαδικασιών στις οποίες είναι πιθανή η έλευση και βλαβών, καθώς και η ταυτόχρονη παρακολούθηση πολλαπλών χαρακτηριστικών ποιότητας σε διαδικασίες πιθανής επίδρασης πολλών συστηματικών αιτιών θέσης και διασποράς.•Όλα τα προτεινόμενα πλήρως δυναμικά διαγράμματα έχουν καλύτερη στατιστική και οικονομική συμπεριφορά έναντι των αντίστοιχων λιγότερων δυναμικών. Αυτό οδηγεί σε σημαντική εξοικονόμηση οικονομικών πόρων αλλά και σε αύξηση της εμπιστοσύνης των χρηστών στην προτεινόμενη διαδικασία παρακολούθησης.•Η λανθασμένη θεώρηση μίας συστηματικής αιτίας σε περιπτώσεις όπου στην πράξη είναι πιθανή η επίδραση πολλαπλών συστηματικών αιτιών προσθέτει σημαντικό κόστος στην υπό παρακολούθηση διαδικασία. Το συμπέρασμα αυτό αυξάνει την αναγκαιότητα χρήσης των προτεινόμενων μοντέλων στις σύγχρονες παραγωγικές διαδικασίες, όπου η υπόθεση πιθανής επίδρασης μίας συστηματικής αιτίας είναι, στις περισσότερες περιπτώσεις, μη ρεαλιστική.
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Edgard M. Maboudou‐Tchao, C. Harrison, Sumen Sen · Quality Technology & Quantitative Management · 2022
Recently statistical process control (SPC) started incorporating advanced tools based on statistical learning for process monitoring due to the increasing availability of large and complex data sets. This phenomenon has generated new problems such as monitoring high-dimensional processes. Two well-known techniques used for this purpose are penalized likelihood and support vector-based process control charts. We investigate the support vector data description (SVDD), an effective method used in multivariate statistical process control (MSPC). Next, a least squares analogue to the SVDD, called LS-SVDD, is investigated. LS-SVDD is formulated using equality constraints in the underlying optimization problem which facilitates a fast, closed-form solution. Variable selection charts are penalized likelihood charts that use diagnosis methodologies for the identification of changed variables. Other penalized likelihood methods using Tikhonov regularization were proposed recently. This approach shrinks all process mean estimates towards zero rather than selecting variables, and it yields a closed-form solution of the monitoring statistic. In this article, we compare penalized methods and support vector methods for Shewhart-type and accumulative-type control charts.
Cited by 10
Bianca Maria Colosimo, Marco Grasso, Federica Garghetti · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2020
The industrial breakthrough of novel manufacturing paradigms, like \nadditive manufacturing, enables the production of innovative shapes, characterized \nby complex 3D structures going beyond more 2.5D free-form surfaces and other \ngeometries produced with traditional methods. This opens novel challenges for \nproduct qualification and statistical process monitoring. This study focuses on a \ncategory of complex shapes known as lattice structures (or metamaterials), where a \nunit cell regularly repeats in space to achieve advanced mechanical and functional \nperformances. We present a novel solution for statistical process monitoring of these \nstructures, aimed at identifying local out-of-control cells within- and between-parts.
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Ingabire Mukamiza, Kizito Mutabazi, Ndayishimiye Nkubamoto · Zenodo (CERN European Organization for Nuclear Research) · 2013
This study examines process-control systems in Rwanda to assess their impact on efficiency gains. A difference-in-differences (DiD) model will be employed to analyse the effectiveness of process-control systems in Rwanda. This statistical method compares changes over time between treatment and control groups. The DiD analysis revealed a significant increase in efficiency gains when using advanced monitoring tools, with an estimated gain of 15% compared to traditional methods. Advanced process-control systems have been shown to enhance operational efficiencies in Rwandan industries, particularly those utilising automated and digital control mechanisms. Rwanda should consider adopting more sophisticated monitoring technologies to improve overall efficiency across various sectors. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
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Ingabire Mukamiza, Kizito Mutabazi, Ndayishimiye Nkubamoto · Zenodo (CERN European Organization for Nuclear Research) · 2013
This study examines process-control systems in Rwanda to assess their impact on efficiency gains. A difference-in-differences (DiD) model will be employed to analyse the effectiveness of process-control systems in Rwanda. This statistical method compares changes over time between treatment and control groups. The DiD analysis revealed a significant increase in efficiency gains when using advanced monitoring tools, with an estimated gain of 15% compared to traditional methods. Advanced process-control systems have been shown to enhance operational efficiencies in Rwandan industries, particularly those utilising automated and digital control mechanisms. Rwanda should consider adopting more sophisticated monitoring technologies to improve overall efficiency across various sectors. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
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Debón, A., Carlos Garcia-Díaz, J. · RePEc: Research Papers in Economics
Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures.
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Jian Li, Fugee Tsung · 2013
The service industry has become increasingly important in our daily lives and global economies, and improving service quality will have a significant social and economical impact. The massive amount of data readily available provides us with opportunities to integrate advanced statistical methodologies with system knowledge to better model and control the quality of service sys-tems. Monitoring and quick detection of abnormal activities have become critical in a variety of service industries that have complex operation systems and process huge amounts of data. However, most conventional statistical process control techniques cannot effectively handle the monitoring and surveillance of such a large amount of data on a real-time basis. This paper in-dicates the importance of and sheds some lights on developing statistical monitoring and control methodologies for the service sector. It focuses on three fundamental issues: (1) the monitoring of feedback data arising from customers ’ perception of service quality; (2) the monitoring and controlling metrics of operational performance in service systems; and (3) the monitoring of the functional relationship between operational determinants and the service quality perceived by end customers. In addition, we will elaborate on the first issue by reviewing some recently developed results.
Cited by 2
Michael Dudzic, Vit Vaculik, Ivan Miletić · 2003
Multivariate statistical technologies, the principal components analysis and projection to latent structures, are data modeling technologies based on advanced multivariable statistical methods. These methods are capable of: analyzing process data; building predictive models and providing SPC functionality by extracting information from all process and quality data from an operation simultaneously. Multivariate statistical methods are especially powerful techniques for analyzing industrial data sets that have the following characteristics: higher dimensionality; high collinearity; noisy; and with some missing data. The application of these methods have been successfully done at Dofasco since 1993 to analyze data for a variety of purposes, develop online predictive models, and develop online process monitoring systems. An online application is described to illustrate the advantages of this technology.
Cited by 3
T. N. Goh, Min Xie · Total Quality Management · 1994
In today's manufacturing environment, product quality and process productivity have reached a level at which a new quality management philosophy would be useful to maintain an organization's competitive edge. New techniques are also needed as conventional statistical process management procedures would encounter difficulties in their application at this level. In this paper, a ‘do it better each time’ strategy is advanced to replace the ‘do it right the first time’ concept. Adaptation of statistical tools for quality monitoring and control in the light of the new philosophy in a near-zero defect environment is also discussed.
Cited by 17
Manabu Kano Koji, Koji Nagao A, Shinji Hasebe A et al. · 2007
Multivariate statistical process control (MSPC) has been successfully applied to chemical processes. In order to improve the performance of fault detection, two kinds of advanced methods, known as moving principal component analysis (MPCA) and DISSIM, have been proposed. In MPCA and DISSIM, an abnormal operation can be detected by monitoring the directions of principal components (PCs) and the degree of dissimilarity between data sets, respectively. Another important extension of MSPC was made by using multiscale PCA (MS-PCA). In the present work, the characteristics of several monitoring methods are investigated. The monitoring performances are compared with using simulated data obtained from the Tennessee Eastman process. The results show that the advanced methods can outperform the conventional method. Furthermore, the advantage of MPCA and DISSIM over conventional MSPC (cMSPC) and that of the multiscale method are combined, and the new methods known as MS-MPCA and MS-DISSIM are pro...
Cited by 0
S. Shawn Lee, Chenhui Shao, Tae Hyung Kim et al. · 2014
Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and post-weld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.
Cited by 13
S. Shawn Lee, Chenhui Shao, Tae Hyung Kim et al. · ASME Press eBooks · 2017
Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and post-weld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.
Cited by 0
Radosław Wolniak, Wies Grebski · Scientific Papers of Silesian University of Technology Organization and Management Series · 2023
Purpose: The purpose of this publication is to present the usage of Statistical Process Control (SPC) approach in Industry 4.0 conditions. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of Statistical Process Control (SPC) with Industry 4.0 signifies a transformative shift in quality management, elevating SPC from a conventional monitoring tool to a proactive force in contemporary manufacturing. Originally employed for ensuring consistent quality through process monitoring, SPC's role has been redefined in the Industry 4.0 era, utilizing data analytics, real-time monitoring, and connectivity to offer a comprehensive understanding of the manufacturing ecosystem. Enabled by the Internet of Things (IoT), SPC gains real-time insights into production processes, crucial for swift anomaly identification and issue resolution. Advanced analytics and artificial intelligence enhance SPC's predictive capabilities, enabling proactive measures to maintain product quality. This publication underscores the significance of SPC in Industry 4.0 conditions, emphasizing its roots in statistical principles, systematic process control, and the distinction between common and special cause variations. The integration of SPC with Industry 4.0 and Quality 4.0 leverages technologies for enhanced quality management, emphasizing real-time data monitoring and collaborative, data-driven approaches. While Table 2 outlines specific aspects of SPC integration, highlighting its versatility, Table 3 enumerates the advantages, emphasizing improved visibility and predictive quality management. However, challenges such as data security concerns and technology integration complexity, outlined in Table 4, necessitate strategic solutions. In conclusion, this integration represents a pivotal advancement, positioning SPC as an indispensable asset for organizations seeking quality excellence, operational efficiency, and resilience in the dynamic landscape of modern manufacturing. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of Statistical process Control in Industry 4.0 conditions. Keywords: Industry 4.0; Quality 4.0, quality management; quality methods, SPC, Statistical Process Control. Category of the paper: literature review.
Cited by 3Open access
Majid Khan, Zahid Rasheed, Syed Masroor Anwar et al. · Mathematical Problems in Engineering · 2023
Homogeneously weighted moving average (HWMA) charts have recently achieved popularity for monitoring small changes in process parameters (location and/or dispersion). Furthermore, the DHWMA (double HWMA) and THWMA (triple HWMA) are the advanced versions of the HWMA charts. The HWMA chart for the process dispersion is designed to detect only the upward (one‐sided) shift (i.e., process deterioration). Employing a two‐sided chart for concurrently detecting both process improvement and process deterioration is an important aspect of statistical process monitoring. By taking this point as motivation, one and two‐sided THWMA charts (symbolized as the THWMAV) are proposed for monitoring the process dispersion. The Monte Carlo simulations are performed to investigate the performance behavior of the THWMAV charts in terms of certain performance indicators, including ARL, SDRL, EQL, RARL, and PCI. The comparison among the THWMAV versus existing charts (DHWMAV, HWMAV, TEWMAV, DEWMAV, and EWMAV) indicates that the THWMAV charts outperform the existing charts. Finally, a dataset is also analyzed to illustrate the implementation of the THWMAV charts.
Cited by 2Open access
Tae‐Yeoul Yun, Fan Li-jun, A. Qingang et al. · 2016
Studying and exploring variance regularity of the current signal in the process of contamination flashover development is important and helpful for developing flashover on-line monitoring system and studying mechanism of surface flashover in advance. In this paper, five statistical index computed from leakage current envelope such as mean, standard deviation, kurtosis, skewness and third harmonic distortion factor were selected as analysis objects. Through considering them as samples from five random processes, the one-step state transition matrix of the random processes was calculated and the state transition process was analyzed. The results show that surface arc discharge process has some statistical regularity, and single index can't recognize discharge state. The research methods and ideas in this paper were helpful for advanced research on discharge process, setting up its model and study on anti-contamination flashover technology in the future.
Cited by 1