Performance Assessment for Process Monitoring and Fault Detection Methods
Title | Performance Assessment for Process Monitoring and Fault Detection Methods PDF eBook |
Author | Kai Zhang |
Publisher | Springer |
Pages | 164 |
Release | 2016-10-04 |
Genre | Computers |
ISBN | 3658159715 |
The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes.
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Title | Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods PDF eBook |
Author | Chris Aldrich |
Publisher | Springer Science & Business Media |
Pages | 388 |
Release | 2013-06-15 |
Genre | 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.
Chemical Process Performance Evaluation
Title | Chemical Process Performance Evaluation PDF eBook |
Author | Ali Cinar |
Publisher | CRC Press |
Pages | 341 |
Release | 2007-01-11 |
Genre | Science |
ISBN | 1420020102 |
The latest advances in process monitoring, data analysis, and control systems are increasingly useful for maintaining the safety, flexibility, and environmental compliance of industrial manufacturing operations. Focusing on continuous, multivariate processes, Chemical Process Performance Evaluation introduces statistical methods and modeling te
Process Control Performance Assessment
Title | Process Control Performance Assessment PDF eBook |
Author | Andrzej Ordys |
Publisher | Springer Science & Business Media |
Pages | 341 |
Release | 2007-05-19 |
Genre | Technology & Engineering |
ISBN | 1846286247 |
This book is a practical guide to the application of control benchmarking to real, complex, industrial processes. The variety of industrial case studies gives the benchmarking ideas presented a robust real-world attitude. The book deals with control engineering principles and economic and management aspects of benchmarking. It shows the reader how to avoid common problems in benchmarking and details the benefits of effective benchmarking.
Process Monitoring and Fault Diagnosis Based on Multivariable Statistical Analysis
Title | Process Monitoring and Fault Diagnosis Based on Multivariable Statistical Analysis PDF eBook |
Author | Xiangyu Kong |
Publisher | Springer Nature |
Pages | 324 |
Release | |
Genre | |
ISBN | 981998775X |
Data-Driven Fault Detection for Industrial Processes
Title | Data-Driven Fault Detection for Industrial Processes PDF eBook |
Author | Zhiwen Chen |
Publisher | Springer |
Pages | 124 |
Release | 2017-01-02 |
Genre | Technology & Engineering |
ISBN | 3658167564 |
Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.
Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes
Title | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes PDF eBook |
Author | Evan L. Russell |
Publisher | Springer Science & Business Media |
Pages | 193 |
Release | 2012-12-06 |
Genre | Science |
ISBN | 1447104099 |
Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis. The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.