Author : Nitin Mannikar
Publisher :
Page : 238 pages
File Size : 23,84 MB
Release : 1994
Category : Expert systems (Computer science)
ISBN :
Author : Alibek Kopbayev
Publisher :
Page : pages
File Size : 24,5 MB
Release : 2022
Category :
ISBN :
This thesis proposes a novel approach to fault detection and diagnosis (FDD) that is focused on artificial neural network (ANN). Unlike traditional methods for FDD, neural networks can take advantage of large amounts of complex process data and extract core features to help detect and diagnose faults. In the first part of this work, a hybrid model was developed to improve efficiency and feasibility of neural networks by combining Kernel Principal Analysis (kPCA) and deep neural network. The hybrid model was successfully validated by Tennessee Eastman Process. The second part of the research focuses on a specific application to gas leak detection and classification. In this scenario, a convolutional network (ConvNet) was used as a feature extraction tool prior to network training due to the visual nature of data. The model was shown to accurately predict leaks and leak sizes; furthermore, further model optimizations were performed and evaluated. The proposed approach is superior to other FDD approaches due to its performance and optimization flexibility.
Author : Józef Korbicz
Publisher : Springer Science & Business Media
Page : 936 pages
File Size : 44,51 MB
Release : 2012-12-06
Category : Computers
ISBN : 3642186157
This comprehensive work presents the status and likely development of fault diagnosis, an emerging discipline of modern control engineering. It covers fundamentals of model-based fault diagnosis in a wide context, providing a good introduction to the theoretical foundation and many basic approaches of fault detection.
Author : Rolf Isermann
Publisher : Springer Science & Business Media
Page : 500 pages
File Size : 34,70 MB
Release : 2005-10-13
Category : Technology & Engineering
ISBN : 9783540241126
With increasing demands for efficiency and product quality plus progress in the integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.
Author : Richard J. Fickelscherer
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 14,32 MB
Release : 2024-02-21
Category : Science
ISBN : 111982589X
Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading experts Guidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and more Comprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.
Author : P.S. Dhurjati
Publisher : Pergamon
Page : 340 pages
File Size : 37,25 MB
Release : 1993-04-13
Category : Science
ISBN :
Addresses the application of quality management to the chemical process industry, in about 50 selected papers from an April 1991 symposium in Newark, Delaware. They discuss strategies for the detection and diagnosis of process faults; modeling, validation, and interpretation of process trends; neural networks in process supervision and fault diagnosis; and other aspects. Reproduced from the authors' copies. Annotation copyright by Book News, Inc., Portland, OR
Author : Chris Aldrich
Publisher : Springer Science & Business Media
Page : 388 pages
File Size : 50,32 MB
Release : 2013-06-15
Category : Computers
ISBN : 1447151852
This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.
Author : Tamiru Alemu Lemma
Publisher : Springer
Page : 283 pages
File Size : 15,50 MB
Release : 2017-12-30
Category : Technology & Engineering
ISBN : 3319718711
This book provides a hybrid approach to fault detection and diagnostics. It presents a detailed analysis related to practical applications of the fault detection and diagnostics framework, and highlights recent findings on power plant nonlinear model identification and fault diagnostics. The effectiveness of the methods presented is tested using data acquired from actual cogeneration and cooling plants (CCPs). The models presented were developed by applying Neuro-Fuzzy (NF) methods. The book offers a valuable resource for researchers and practicing engineers alike.
Author : Duc T. Pham
Publisher : Springer Science & Business Media
Page : 212 pages
File Size : 45,99 MB
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 1447114981
Although the tenn quality does not have a precise and universally accepted definition, its meaning is generally well understood: quality is what makes the difference between success and failure in a competitive world. Given the importance of quality, there is a need for effective quality systems to ensure that the highest quality is achieved within given constraints on human, material or financial resources. This book discusses Intelligent Quality Systems, that is quality systems employing techniques from the field of Artificial Intelligence (AI). The book focuses on two popular AI techniques, expert or knowledge-based systems and neural networks. Expert systems encapsulate human expertise for solving difficult problems. Neural networks have the ability to learn problem solving from examples. The aim of the book is to illustrate applications of these techniques to the design and operation of effective quality systems. The book comprises 8 chapters. Chapter 1 provides an introduction to quality control and a general discussion of possible AI-based quality systems. Chapter 2 gives technical information on the key AI techniques of expert systems and neural networks. The use of these techniques, singly and in a combined hybrid fonn, to realise intelligent Statistical Process Control (SPC) systems for quality improvement is the subject of Chapters 3-5. Chapter 6 covers experimental design and the Taguchi method which is an effective technique for designing quality into a product or process. The application of expert systems and neural networks to facilitate experimental design is described in this chapter.
Author : L.H. Chiang
Publisher : Springer Science & Business Media
Page : 281 pages
File Size : 46,26 MB
Release : 2012-12-06
Category : 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.
