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Fault Detection and Diagnosis Via Improved Statistical Process Control

Author : Noorlisa Harun

Publisher : LAP Lambert Academic Publishing

Page : 172 pages

File Size : 31,88 MB

Release : 2012-07

Category :

ISBN : 9783659177651

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Book Description

Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis (FDD). Currently, contribution plots are used as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly diagnose the fault, it just provides greater insight into possible causes and thereby narrow down the search. Hence, the cause of the faults cannot be found in a straightforward manner. Therefore, this study is conducted to introduce a new approach for detecting and diagnosing fault via correlation technique. Multivariate analysis technique i.e Principal Component Analysis, PCA and Partial Correlation Analysis, PCorrA are utilized to determine the correlation coefficient between quality variables and process variables. A precut multicomponent distillation column that has been installed with controllers is used as the study unit operation. Improved SPC method is implemented to detect and diagnose various kinds of faults, which occur in the process. Individual charting technique such as Shewhart, Exponential Weight Moving Average (EWMA) and Moving Average and Moving Range (MAMR) charts are used to facilitate the FDD.









Multivariate Statistical Process Control for Fault Detection and Diagnosis

Author : Mohamed Ouhsain

Publisher :

Page : 0 pages

File Size : 10,34 MB

Release : 2007

Category :

ISBN :

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Book Description

The great challenge in quality control and process management is to devise computationally efficient algorithms to detect and diagnose faults. Currently, univariate statistical process control is an integral part of basic quality management and quality assurance practices used in the industry. Unfortunately, most data and process variables are inherently multivariate and need to be modelled accordingly. Major barriers such as higher complexity and harder interpretation have limited their application by both engineers and operators. Motivated by the lack of techniques dedicated in monitoring highly correlated data, we introduce in this thesis new multivariate statistical process control charts using robust statistics, machine learning, and pattern recognition techniques to propose our algorithms. The core idea behind our proposed techniques is to fully explore the advantages/limitations under a wide array of environments, and to also take advantage of the latter to develop a theoretically rigorous and computationally feasible methodology for multivariate statistical process control. Illustrating experimental results demonstrate a much improved performance of the proposed approaches in comparison with existing methods currently used in the analysis and monitoring of multivariate data.





Fault Detection and Diagnosis in Industrial Systems

Author : L.H. Chiang

Publisher : Springer Science & Business Media

Page : 281 pages

File Size : 24,11 MB

Release : 2012-12-06

Category : Technology & Engineering

ISBN : 1447103475

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Book Description

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.



Fault Detection, Diagnosis and Prognosis

Author : Fausto Pedro García Márquez

Publisher : BoD – Books on Demand

Page : 177 pages

File Size : 24,98 MB

Release : 2020-02-05

Category : Mathematics

ISBN : 1789842131

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Book Description

This book presents the main concepts, state of the art, advances, and case studies of fault detection, diagnosis, and prognosis. This topic is a critical variable in industry to reach and maintain competitiveness. Therefore, proper management of the corrective, predictive, and preventive politics in any industry is required. This book complements other subdisciplines such as economics, finance, marketing, decision and risk analysis, engineering, etc. The book presents real case studies in multiple disciplines. It considers the main topics using prognostic and subdiscipline techniques. It is essential to link these topics with the areas of finance, scheduling, resources, downtime, etc. to increase productivity, profitability, maintainability, reliability, safety, and availability, and reduce costs and downtime. Advances in mathematics, modeling, computational techniques, dynamic analysis, etc. are employed analytically. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support the analysis of prognostic problems with defined constraints and requirements. The book is intended for graduate students and professionals in industrial engineering, business administration, industrial organization, operations management, applied microeconomics, and the decisions sciences, either studying maintenance or needing to solve large, specific, and complex maintenance management problems as part of their jobs. The work will also be of interest to researches from academia.



Fault-Tolerant Process Control

Author : Prashant Mhaskar

Publisher : Springer Science & Business Media

Page : 279 pages

File Size : 30,26 MB

Release : 2012-11-27

Category : Technology & Engineering

ISBN : 1447148088

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Book Description

Fault-Tolerant Process Control focuses on the development of general, yet practical, methods for the design of advanced fault-tolerant control systems; these ensure an efficient fault detection and a timely response to enhance fault recovery, prevent faults from propagating or developing into total failures, and reduce the risk of safety hazards. To this end, methods are presented for the design of advanced fault-tolerant control systems for chemical processes which explicitly deal with actuator/controller failures and sensor faults and data losses. Specifically, the book puts forward: · A framework for detection, isolation and diagnosis of actuator and sensor faults for nonlinear systems; · Controller reconfiguration and safe-parking-based fault-handling methodologies; · Integrated-data- and model-based fault-detection and isolation and fault-tolerant control methods; · Methods for handling sensor faults and data losses; and · Methods for monitoring the performance of low-level PID loops. The methodologies proposed employ nonlinear systems analysis, Lyapunov techniques, optimization, statistical methods and hybrid systems theory and are predicated upon the idea of integrating fault-detection, local feedback control, and supervisory control. The applicability and performance of the methods are demonstrated through a number of chemical process examples. Fault-Tolerant Process Control is a valuable resource for academic researchers, industrial practitioners as well as graduate students pursuing research in this area.



Fault Detection Via Shewhart and Ewma Charts

Author : Mohd Hafiz Mohd Saad

Publisher :

Page : 36 pages

File Size : 40,50 MB

Release : 2006

Category : Process control

ISBN :

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Book Description

Statistical Process Control is a technique that concerned with monitoring process capability and process stability. Statistical Control is the condition describing a process from which all special causes have been removed, evidenced on a control chart by the absence of points beyond the control limits and by the absence of non-random patterns or trends within the control limits. Principle Component Analysis, PCA is one of the multivariate analysis techniques to improve the traditional SPC chart by determined the cross correlation between process variables and quality variables. Despite improve Statistical Process Control, SPC chart and determined the correlation between process and quality variables, comparison of the performance of SPC chart should be taken to provide the best chart to detect the fault in the process. For achieve the objective, fIrst step is to identify process and quality variables. The faults can be detected when after Normal Operating Condition data were create to develop control limit of improved SPC chart. The correlation between process and quality variables have to determine to certify the variables are correlated. From the results shows that EWMA chart can detect 77.5% of known faults occurs on reflux stream and Shewhart chart has 87.5%. The result shows the performance between of two charts which give the Shewhart chart has the best performance in detected the faults.