Read more
Zusatztext "Most texts that attempt to combine SPC or SPM (statistical process monitoring) with automated control methods fail to incorporate multivariate methods as well. This text does an excellent job of covering all the bases in that regard . . . I highly recommend this text for chemical engineers and statisticians interested in learning how statistical methods can be integrated with process control methods." - Dean V. Neubauer! Corning Inc.! in Technometrics! February 2008! Vol. 50! No. 1 Informationen zum Autor Ahmet Palazoglu, Ali Cinar, Ferhan Kayihan Klappentext Focusing on continuous! multivariate processes! Chemical Process Performance Evaluation introduces statistical methods and modeling techniques for process monitoring! process and controller performance evaluation! and fault diagnosis. The book covers empirical modeling development techniques! modeling process signals for trend analysis! sensor failure detection and diagnosis! controller performance assessment! process performance evaluation! and data analysis techniques for web and sheet processes. Balancing practice and theory! the book integrates several techniques to facilitate practical applications. Case studies illustrate the implementation of methods presented throughout. Zusammenfassung Focusing on continuous, multivariate processes, this book introduces statistical methods and modeling techniques for process monitoring, process and controller performance evaluation, and fault diagnosis. It covers empirical modeling development techniques, modeling process signals for trend analysis, and sensor failure detection and diagnosis. Inhaltsverzeichnis Preface Nomenclature INTRODUCTION Motivation and Historical Perspective Outline UNIVARIATE SPM Statistics Concepts Univariate SPM Techniques Monitoring Tools for Autocorrelated Data Limitations of Univariate SPM Methods STATISTICAL METHODS FOR PERFORMANCE EVALUATION Principal Components Analysis Canonical Variates Analysis Independent Component Analysis Contribution Plots Linear Methods for Diagnosis Nonlinear Methods for DiagnosisEMPIRICAL MODEL DEVELOPMENT Regression Models PCA Models PLS Regression Models Input-Output Models of Dynamic Processes State-Space Models MONITORING OF MULTIVARIATE PROCESSES SPM Methods Based on PCA SPM Methods Based on PLS SPM Using Dynamic Process Models Other MSPM Techniques CHARACTERIZATION OF PROCESS SIGNALS Wavelets Filtering and Outlier Detection Signal Representation by Fuzzy Triangular Episodes Development of Markovian Models Wavelet-Domain Hidden Markov Models PROCESS FAULT DIAGNOSIS Fault Diagnosis Using Triangular Episodes and HMMs Fault Diagnosis Using Wavelet-Domain HMMs Fault Diagnosis Using HMMs Fault Diagnosis Using Contribution Plots Fault Diagnosis with Statistical Methods Fault Diagnosis Using SVM Fault Diagnosis with Robust Techniques SENSOR FAILURE DETECTION AND DIAGNOSIS Sensor FDD Using PLS and CVSS Models Real-Time Sensor FDD Using PCA-Based Techniques CONTROLLER PERFORMANCE MONITORING Single-Loop CPM Multivariable Controller Performance Monitoring CPM for MPC WEB AND SHEET PROCESSES Traditional Data Analysis Orthogonal Decomposition of Profile Data Controller Performance Bibliography Index*Each Chapter Contains a Summary Section ...
