Download or read book Process Monitoring and Fault Diagnosis Based on Multivariable Statistical Analysis written by Xiangyu Kong and published by Springer Nature. This book was released on with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi state PLS Based Data driven Predictive Modeling for Continuous Process Analytics written by Vinay Kumar (master of science in engineering.) and published by . This book was released on 2012 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today's process control industry, which is extensively automated, generates huge amounts of process data from the sensors used to monitor the processes. These data if effectively analyzed and interpreted can give a clearer picture of the performance of the underlying process and can be used for its proactive monitoring. With the great advancements in computing systems a new genre of process monitoring and fault detection systems are being developed which are essentially data-driven. The objectives of this research are to explore a set of data-driven methodologies with a motive to provide a predictive modeling framework and to apply it to process control. This project explores some of the data-driven methods being used in the process control industry, compares their performance, and introduces a novel method based on statistical process control techniques. To evaluate the performance of this novel predictive modeling technique called Multi-state PLS, a patented continuous process analytics technique that is being developed at Emerson Process Management, Austin, some extensive simulations were performed in MATLAB. A MATLAB Graphical User Interface has been developed for implementing the algorithm on the data generated from the simulation of a continuously stirred blending tank. The effects of noise, disturbances, and different excitations on the performance of this algorithm were studied through these simulations. The simulations have been performed first on a steady state system and then applied to a dynamic system .Based on the results obtained for the dynamic system, some modifications have been done in the algorithm to further improve the prediction performance when the system is in dynamic state. Future work includes implementing of the MATLAB based predictive modeling technique to real production data, assessing the performance of the algorithm and to compare with the performance for simulated data.

Download or read book Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes written by Jianjun Shi and published by CRC Press. This book was released on 2006-12-04 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Variability arises in multistage manufacturing processes (MMPs) from a variety of sources. Variation reduction demands data fusion from product/process design, manufacturing process data, and quality measurement. Statistical process control (SPC), with a focus on quality data alone, only tells half of the story and is a passive method, taking corrective action only after variations occur. Learn how the Stream of Variation (SoV) methodology helps reduce or even eliminate variations throughout the entire MMP in Jianjun Shi's Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes. The unified methodology outlined in this book addresses all aspects of variation reduction in a MMP, which consists of state space modeling, design analysis and synthesis, engineering-driven statistical methods for process monitoring and root-cause diagnosis, and quick failure recovery and defect prevention. Coverage falls into five sections, beginning with a review of matrix theory and multivariate statistics followed by variation propagation modeling with applications in assembly and machining processes. The third section focuses on diagnosing the sources of variation while the fourth section explains design methods to reduce variability. The final section assembles advanced SoV-related topics and the integration of quality and reliability. Introducing a powerful and industry-proven method, this book fuses statistical knowledge with the engineering knowledge of product quality and unifies the design of processes and products to achieve more predictable and reliable manufacturing processes.

Download or read book Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning written by Thorsten Wuest and published by Springer. This book was released on 2015-04-20 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation, chemical and semiconductor). The results, which are reported in detail in this book, confirmed that it is possible to incorporate implicit process intra- and interrelations on both a process and programme level by applying SVM-based feature ranking. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control. Importantly, the method is neither limited to specific products, manufacturing processes or systems, nor by specific quality concepts.

Download or read book Smart System Monitoring for High Dimensional Multistage Manufacturing Processes written by Mohammadhossein Amini and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This research studies a system-wide approach to monitor product quality in real time to avoid manufacturing defects in high dimensional multistage processes. Traditional control charts have been widely used in various manufacturing industries due to their simplicity. However, in today's complex manufacturing processes, these charts are not efficient anymore. A complex manufacturing process may include multiple stages with sensors embedded throughout the processes that generate a huge amount of data in high dimensions. Since the numbers of stages and parameters are usually very large, traditional control charts are incapable of handling a multistage high-dimensional problem mainly due to the problem of false alarm rates of simultaneous monitoring. Industry 4.0 and Internet of things (IOT) provides opportunities to achieve better quality products toward a zero defect system. Currently, data is either thrown away or stored in unused databases. In a inefficient approach called "fire-fighting", when there is a decline in the quality, process engineers go back to archived process data to figure out the problem. However, due to various reasons such as messy and unclean data, outdated data, and common manufacturing data features such as complexity and dimensionality issues, this process may take a long time. In the best cases, researchers provide classification-based process monitoring techniques to use the manufacturing data. However, the state-of-the-art classification-based process monitoring techniques usually provide quality predictions at the end of the manufacturing process and provide no chance to fix the problem. In addition, knowing high dimensional, unbalanced, and newly released manufacturing data, the literature is largely silent on providing a comprehensive study addressing those issues. While addressing above mentioned challenges, the proposed research delivers a stage-wise process monitoring which provides plenty of time for engineers to fix the process before the last point. Then, based on the results from the predictive models, adjustable process parameters can be altered to avoid potential defects. The proposed research relies on predictive models which are built on a series of classifiers. The proposed research is implemented in two different manufacturing application - additive manufacturing (AM) and semiconductor production. The proposed research in the AM industry called the Multi-Layer Classification Process Monitoring (MLCPM) is applied in the Laser Powder Bed Fusion (LPBF) metal printing process. In the semiconductor manufacturing industry, we applied the proposed method on a very imbalanced high dimensional production data called SECOM (SEmiCOnductor Manufacturing) which is publicly available through the UCI repository lab. In this study, we examined various classification models and sampling approaches to find the best results in terms of specific metrics chosen regarding the imbalanced nature of the problem. The applied case studies show the effectiveness of the proposed framework in terms of accurately predict the real-time production quality state. The chance of predicting the quality of the process before the last step provides chances to reduce the waste and save cost and time in the production systems.

Download or read book Comprehensive Chemometrics written by Steven Brown and published by Elsevier. This book was released on 2020-05-26 with total page 2948 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Chemometrics, Second Edition, Four Volume Set features expanded and updated coverage, along with new content that covers advances in the field since the previous edition published in 2009. Subject of note include updates in the fields of multidimensional and megavariate data analysis, omics data analysis, big chemical and biochemical data analysis, data fusion and sparse methods. The book follows a similar structure to the previous edition, using the same section titles to frame articles. Many chapters from the previous edition are updated, but there are also many new chapters on the latest developments. Presents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009 Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easily Written by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audience Presents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009 Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easily Written by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audience

Download or read book Advanced Process Data Analytics written by Weike Sun (Ph. D.) and published by . This book was released on 2020 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Process data analytics is the application of statistics and related mathematical tools to data in order to understand, develop, and improve manufacturing processes. There have been growing opportunities in process data analytics because of advances in machine learning and technologies for data collection and storage. However, challenges are encountered because of the complexities of manufacturing processes, which often require advanced analytical methods. In this thesis, two areas of application are considered. One is the construction of predictive models that are useful for process design, optimization, and control. The other area of application is process monitoring to improve process efficiency and safety. In the first area of study, a robust and automated approach for method selection and model construction is developed for predictive modeling. Two common challenges when building data-driven process models are addressed: the high diversity in data quality and how to select from a wide variety of methods. The proposed approach combines best practices with data interrogation to facilitate consistent application and continuous improvement of tools and decision making. The second area of study focuses on process monitoring for complex manufacturing systems, which includes fault detection, identification, and classification. Four sets of algorithms are developed to address limitations of traditional monitoring methods. The first set provides the optimal strategy for Gaussian linear processes, including deep understanding of the process monitoring structure and optimal fault detection based on a probabilistic formulation. The second set aims at building a self-learning fault detection system for changing normal operating conditions. The third set is developed based on information-theoretic learning to address limitations of second-order statistical learning for both fault detection and classification. The fourth set tackles the problem of nonlinear and dynamic process monitoring. The proposed methodologies and algorithms are tested on several case studies where the value of advanced process data analytics is demonstrated.

Download or read book Partial Least Squares Path Modeling written by Hengky Latan and published by Springer. This book was released on 2017-11-03 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book presents the recent developments in partial least squares-path modeling (PLS-PM) and provides a comprehensive overview of the current state of the most advanced research related to PLS-PM. The first section of this book emphasizes the basic concepts and extensions of the PLS-PM method. The second section discusses the methodological issues that are the focus of the recent development of the PLS-PM method. The third part discusses the real world application of the PLS-PM method in various disciplines. The contributions from expert authors in the field of PLS focus on topics such as the factor-based PLS-PM, the perfect match between a model and a mode, quantile composite-based path modeling (QC-PM), ordinal consistent partial least squares (OrdPLSc), non-symmetrical composite-based path modeling (NSCPM), modern view for mediation analysis in PLS-PM, a multi-method approach for identifying and treating unobserved heterogeneity, multigroup analysis (PLS-MGA), the assessment of the common method bias, non-metric PLS with categorical indicators, evaluation of the efficiency and accuracy of model misspecification and bootstrap parameter recovery in PLS-PM, CB-SEM, and the Bollen-Stine methods and importance-performance map analysis (IPMA) for nonlinear relationships. This book will be useful for researchers and practitioners interested in the latest advances in PLS-PM as well as master and Ph.D. students in a variety of disciplines using the PLS-PM method for their projects.

Download or read book Hot Melt Extrusion written by Dennis Douroumis and published by John Wiley & Sons. This book was released on 2012-04-24 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hot-melt extrusion (HME) - melting a substance and forcing it through an orifice under controlled conditions to form a new material - is an emerging processing technology in the pharmaceutical industry for the preparation of various dosage forms and drug delivery systems, for example granules and sustained release tablets. Hot-Melt Extrusion: Pharmaceutical Applications covers the main instrumentation, operation principles and theoretical background of HME. It then focuses on HME drug delivery systems, dosage forms and clinical studies (including pharmacokinetics and bioavailability) of HME products. Finally, the book includes some recent and novel HME applications, scale -up considerations and regulatory issues. Topics covered include: principles and die design of single screw extrusion twin screw extrusion techniques and practices in the laboratory and on production scale HME developments for the pharmaceutical industry solubility parameters for prediction of drug/polymer miscibility in HME formulations the influence of plasticizers in HME applications of polymethacrylate polymers in HME HME of ethylcellulose, hypromellose, and polyethylene oxide bioadhesion properties of polymeric films produced by HME taste masking using HME clinical studies, bioavailability and pharmacokinetics of HME products injection moulding and HME processing for pharmaceutical materials laminar dispersive & distributive mixing with dissolution and applications to HME technological considerations related to scale-up of HME processes devices and implant systems by HME an FDA perspective on HME product and process understanding improved process understanding and control of an HME process with near-infrared spectroscopy Hot-Melt Extrusion: Pharmaceutical Applications is an essential multidisciplinary guide to the emerging pharmaceutical uses of this processing technology for researchers in academia and industry working in drug formulation and delivery, pharmaceutical engineering and processing, and polymers and materials science. This is the first book from our brand new series Advances in Pharmaceutical Technology. Find out more about the series here.

Download or read book INFORMS Annual Meeting written by Institute for Operations Research and the Management Sciences. National Meeting and published by . This book was released on 2008 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fault Detection and Diagnosis in Industrial Systems written by L.H. Chiang and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.

Download or read book Handbook of Partial Least Squares written by Vincenzo Esposito Vinzi and published by Springer Science & Business Media. This book was released on 2010-03-10 with total page 791 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

Download or read book Data Mining and Predictive Analytics written by Daniel T. Larose and published by John Wiley & Sons. This book was released on 2015-02-19 with total page 827 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Download or read book Multi and Megavariate Data Analysis Basic Principles and Applications written by L. Eriksson and published by Umetrics Academy. This book was released on 2013-07-01 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: To understand the world around us, as well as ourselves, we need to measure many things, many variables, many properties of the systems and processes we investigate. Hence, data collected in science, technology, and almost everywhere else are multivariate, a data table with multiple variables measured on multiple observations (cases, samples, items, process time points, experiments). This book describes a remarkably simple minimalistic and practical approach to the analysis of data tables (multivariate data). The approach is based on projection methods, which are PCA (principal components analysis), and PLS (projection to latent structures) and the book shows how this works in science and technology for a wide variety of applications. In particular, it is shown how the great information content in well collected multivariate data can be expressed in terms of simple but illuminating plots, facilitating the understanding and interpretation of the data. The projection approach applies to a variety of data-analytical objectives, i.e., (i) summarizing and visualizing a data set, (ii) multivariate classification and discriminant analysis, and (iii) finding quantitative relationships among the variables. This works with any shape of data table, with many or few variables (columns), many or few observations (rows), and complete or incomplete data tables (missing data). In particular, projections handle data matrices with more variables than observations very well, and the data can be noisy and highly collinear. Authors: The five authors are all connected to the Umetrics company (www.umetrics.com) which has developed and sold software for multivariate analysis since 1987, as well as supports customers with training and consultations. Umetrics' customers include most large and medium sized companies in the pharmaceutical, biopharm, chemical, and semiconductor sectors.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1983 with total page 1368 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Matrix Pencils written by B. Kagström and published by Springer. This book was released on 2006-11-15 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Batch Processes written by Ekaterini Korovessi and published by CRC Press. This book was released on 2005-09-26 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reduced time to market, lower production costs, and improved flexibility are critical success factors for batch processes. Their ability to handle variations in feedstock and product specifications has made them key to the operation of multipurpose facilities, and therefore quite popular in the specialty chemical, pharmaceutical, agricultural, and