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Statistical analysis of profile monitoring
A one-of-a-kind presentation of the major achievements in statistical profile monitoring methods Statistical profile monitoring is an area of statistical quality control that is growing in significance for researchers and practitioners, specifically because of its range of applicability across various service and manufacturing settings. Comprised of contributions from renowned academicians and practitioners in the field, Statistical Analysis of Profile Monitoring presents the latest state-of-the-art research on the use of control charts to monitor process and product quality profiles. The book presents comprehensive coverage of profile monitoring definitions, techniques, models, and application examples, particularly in various areas of engineering and statistics. The book begins with an introduction to the concept of profile monitoring and its applications in practice. Subsequent chapters explore the fundamental concepts, methods, and issues related to statistical profile monitoring, with topics of coverage including: Simple and multiple linear profiles Binary response profiles Parametric and nonparametric nonlinear profiles Multivariate linear profiles monitoring Statistical process control for geometric specifications Correlation and autocorrelation in profiles Nonparametric profile monitoring Throughout the book, more than two dozen real-world case studies highlight the discussed topics along with innovative examples and applications of profile monitoring. Statistical Analysis of Profile Monitoring is an excellent book for courses on statistical quality control at the graduate level. It also serves as a valuable reference for quality engineers, researchers and anyone who works in monitoring and improving statistical processes.
eBook
Statistical methods for quality improvement
\"In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include updated coverage of control charts, with newly added tools. The latest research on the monitoring of linear profiles and other types of profiles Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures\"-- Provided by publisher.
Book
Causal Plot: Causal-Based Fault Diagnosis Method Based on Causal Analysis
Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are identified as causes of faults. In this study, the proposed causal plot was applied to fault diagnosis problems of a vinyl acetate monomer (VAM) manufacturing process. The application results showed that the proposed causal plot diagnosed appropriate causes of faults even when conventional contribution plots could not do the same. In addition, we discuss the effects of the presence of a recycle flow on fault diagnosis results based on the analysis result of the VAM process. The proposed causal plot contributes to realizing safe and efficient process operations.
Journal Article
Statistics for Six sigma made easy!
Presents an introduction to the basic principles of the statistical strategy, detailing its tools and techniques, with case studies and advice for obtaining good samples and data and a matrix for choosing the best methods for a particular industry.
Book
Advances in statistical quality control chart techniques and their limitations to cement industry
Sustainability issues are challenging the cement industry due to its high emission of greenhouse gas, intensive energy consumption, and depletion of resources. One of the strategies to mitigate the problem is to improve process control techniques and optimize resources. The objective of this paper is to survey the approach and evolution of statistical process control chart techniques and study their significance and limitations in the case of optimization of cement production. The main research question this study address is \"What are the significances and limitations of statistical process control chart methods to the optimization of cement process?\" The methodology of the study followed the literature survey with meta-analysis and focused on identifying the statistical process control chart design techniques and their application to cement industries. The result of the survey indicated that statistical and mathematical algorithms are encapsulated by advanced soft computing methods; however, still, it is the foundation for advanced process control methods. Moreover, it is found that statistical process control has a theoretical and technical gap in the application of the cement industry. The theoretical gap identified in the literature is that in the case of a complex production system the techniques recognize the occurrence of the out-of-control case in the production process but are not able to identify the cause of variation. The technical gap in the statistical process control techniques is that there are several important theoretical control chart techniques, but they are not researched well on how to apply to the real world.
Journal Article
Integration of multivariate statistical process control and engineering process control: a novel framework
Statistical process control is being used along with classical feedback control systems (which are also termed as Engineering Process Control, EPC) for the purposes of detecting faults and avoiding over adjustment of the processes. This paper evaluates the effectiveness of integrating SPC with EPC for both fault detection and control. A novel framework for fault detection using Multivariate Statistical Process Control (MSPC) is proposed here and illustrated with a case study. The simultaneous application of MSPC control charts to process inputs and outputs or in other words “joint monitoring” of process inputs and outputs is shown here to provide efficient fault detection capabilities. An example of Heating Ventilation and Air Conditioning (HVAC) systems is simulated here and used as a case study to demonstrate the detection capabilities of the proposed framework. Moreover, the capabilities of the proposed framework were enhanced by inclusion of a corrective action scheme, thus leading to a complete control system with fault detection and correction.
Journal Article
A Control Chart Based on A Nonparametric Multivariate Change-Point Model
Phase-II statistical process control (SPC) procedures are designed to detect a change in distribution when a possibly never-ending stream of observations is collected. Several techniques have been proposed to detect a shift in location vector when each observation consists of multiple measurements. These procedures require the user to make assumptions about the distribution of the process readings, to assume that process parameters are known, or to collect a large training sample before monitoring the ongoing process for a change in distribution. We propose a nonparametric procedure for multivariate phase-II statistical process control designed to detect shifts in location vector that relaxes these requirements based on an approximately distribution free multivariate test statistic. This procedure may not be appropriate for some multivariate distributions with unusual dependence structure between vector components. A diagnostic tool that can be used if a historical sample of data is available is provided to assist user in determining if the proposed procedure is appropriate for a given application.
Journal Article
SPC-based model for evaluation of training processes in industrial context
Purpose: This article aims to present successful practices in the management of training processes based on virtual reality and augmented reality, namely a strategy for evaluating the process with the principle of continuous improvement in mind, and monitoring its performance in terms of productivity and waste levels. It is proposed to apply Statistical Process Control (SPC) tools to develop control charts for monitoring individual events (i-charts).Design/methodology/approach: The methodology is based on a case study developed in an industrial project and is guided by a literature review on Work-Based Learning (WBL) and SPC.Findings: The developed work shows that SPC tools are suitable for supporting decision making in situations where the data to be analyzed is generated by human-computer interactions, e.g., involving students and virtual learning environments.Originality/value: The innovative aspect presented in the article lies in the evaluation of the effectiveness of pedagogical resources arranged in simulation environments based on virtual and augmented reality. The accumulated knowledge about the application of SPC in service areas, and others that demand data analysis, reinforces the hypothesis of the suitability of its application in the case presented. This is an original application of SPC, normally used in business processes quality control, but which in this case is applied in an innovative way to the evaluation of industrial training processes, with the same spirit for which it was designed, i.e. to provide the means to manage the quality of a process.
Journal Article