Fault Detection and Diagnosis Via Improved Statistical Process Control

Fault Detection and Diagnosis Via Improved Statistical Process Control
Author: Noorlisa Harun
Publisher: LAP Lambert Academic Publishing
Total Pages: 172
Release: 2012-07
Genre:
ISBN: 9783659177651

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.

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems
Author: L.H. Chiang
Publisher: Springer Science & Business Media
Total Pages: 281
Release: 2012-12-06
Genre: Technology & Engineering
ISBN: 1447103475

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

Fault Detection, Diagnosis and Prognosis
Author: Fausto Pedro García Márquez
Publisher: BoD – Books on Demand
Total Pages: 177
Release: 2020-02-05
Genre: Mathematics
ISBN: 1789842131

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.

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Author: Jing Wang
Publisher: Springer Nature
Total Pages: 277
Release: 2022-01-03
Genre: Technology & Engineering
ISBN: 9811680442

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.