Modeling, Diagnostics and Process Control

Transkrypt

1 Modeling, Diagnostics and Process Control

2 Józef Korbicz and Jan Maciej Kościelny (Eds.) Modeling, Diagnostics and Process Control Implementation in the DiaSter System ABC

3 Prof. Józef Korbicz University of Zielona Góra, Institute of Control & Computation Engineering ul. Podgórna 50, Zielona Góra Poland Prof. Jan Maciej Kościelny Warsaw University of Technology, Institute of Automatic Control and Robotics Ul. Chodkiewicza Warszawa Poland jm.koscielny@mchtr.pw.edu.pl ISBN e-isbn DOI / Library of Congress Control Number: c 2010 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Data supplied by the authors Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India Printed on acid-free paper springer.com

4 Preface The continuous increase in the complexity of modern industrial systems and objects as well as growing reliability demands regarding their operation and control quality are serious challenges for further development of the theory and practice of control and technical diagnostics. Thus modern control systems are complex in the sense of implementing numerous functions, such as process variable processing, digital control, process monitoring and alarm indication, graphic visualization of the course of a process, or data exchange with other systems or databases. Moreover, modern control systems are integrated with management systems, which very often cover production and corporate management problems. Hardware and software structures of control systems of complex processes are decentralized and space distributed. Decentralization consists in dividing system operation into many function units working simultaneously. In integrated systems, software for control and visualization creates one information system with a common database. The main driving force behind the development of modern control systems is rapid evolvement of computer techniques, which have forced the standardization of field networks and programming languages of control systems. The enormous possibilities of technical and program realization of control systems permit significant extension of their functions and tasks, including the introduction of advanced algorithms of process modeling, control and diagnostics. The present book conveys a description of the developed DiaSter system as well as characteristics of advanced original methods of modeling, knowledge discovery, simulator construction, diagnostics, control, and supervision control applied in the system. The system gives the possibility of early recognition of abnormal states of industrial processes and faults or malfunctions of actuators as well as technological and measuring units. The universality of solutions assumed in DiaSter allows its broad application, for example, in power, chemical, pharmaceutical, metallurgical and food industries. The system is a world-scale unique solution, and due to its open architecture it can be connected practically with any other control systems.

5 VI Preface In the main part of the book, analytical and artificial intelligence methods implemented in the DiaSter system are discussed. One of the first chapters is devoted to problems of physical process modeling, fundamental in modern control, diagnostics, or designing and searching for alternative solutions. The known analytical as well as neural, fuzzy and neuro-fuzzy models are presented. Particularly the latter, that is, artificial intelligence methods, are attractive for control systems as they give possibilities for describing nonlinear processes in quite an easy way. In turn, taking into account the fact that modern Distributed Control Systems (DCSs) as well as Supervision Control And Data Acquisition (SCADA) systems allow collecting a huge amount of process data coming from different sources, in another chapter methods and algorithms of knowledge discovery in databases are considered. In DiaSter, process data can be a source of diagnostic knowledge, which obtained with the techniques of knowledge discovery can be used for fault detection and localization of processes and systems. An extensive chapter is devoted to describing diagnostics methods of processes and systems, which can be found useful in industry and were implemented in the DiaSter system. First of all, robust fault detection algorithms designed by employing the adaptive decision threshold approach are discussed. Such thresholds were assigned for fault detection systems with dynamical neural models such as multilayer perceptrons and the Group Method of Data Handling (GMDH). Methods of fault localization are considered mainly with the application of fuzzy logic. The proposed approach is based on inference rules robust with respect to structure changes of diagnosed processes or systems. It includes inference algorithms implemented in DiaSter for single and multiple faults, different ways of dealing with symptoms occurrence delays, or reference algorithms in a hierarchical structure. Moreover, taking into account advantages and disadvantages of methods of symptom diagnostics and the model based approach, in the book a belief-network-based model is presented as well. It is a heuristical model that permits sequential application of methods characteristic for both classes of diagnostic investigation. In another chapter, methods of supervision control implemented in the system are discussed. Elementary structures and algorithms of predictive control, the so-called MPC (Model-based Predictive Control), including the DMC (Dynamic Model Control) algorithm for linear models, are presented. Also, fundamental methods of automatic adjustment of the PID control loop followed by precision adjustment and adaptation are considered. Describing a set point control system, the way of transmitting the set amount generated in the optimization layer of the process working point to the control loop in the direct control layer is presented. The last chapter illustrates the operation of different functions of DiaSter with a simple system of three tanks. The chosen plain example has many educational advantages and gives an excellent possibility for exact studying of fundamental characteristics and possibilities of the DiaSter system. By using system tools, a simulator of the three-tank system, dynamical

6 Preface VII models of the GMDH and the multilayer perceptron type as well as the TSK (Takagi Sugeno Kang) model were built. The realization of diagnostic tasks was shown both for systematic diagnostics of abrupt faults as well as the detection and tracking of the development of slowly increasing faults. Here, the control systems used in the object, that is, traditional PID controllers with automatic adjustment of settings and predictive controllers, are presented as well. The present monograph is in a sense a continuation of our earlier book entitled Fault Diagnosis. Models, Artificial Intelligence, Applications (Springer, 2004), and its first edition was published in Polish by Wydawnictwa Naukowo-Techniczne, WNT, in 2009, (Warsaw, Poland). This english language version is not merely a translation of the original many chapters contain some significant improvements, such as new or extended parts and examples, found especially in Chapter 7. The book presents theoretical and practical results of research into fault diagnosis and control conducted over many years within cooperation between Polish research teams from the Warsaw University of Technology, the University of Zielona Góra, the Silesian University of Technology in Gliwice, and the Technical University of Rzeszów. In the years , the above-mentioned consortium of universities conducted a developmental project entitled Intelligent diagnostic and control assistance system for industrial processes DiaSter. The editors wish to express their gratitude to all authors for preparing joint chapters and for very fruitful collaboration during the editorial process. July 2010 Zielona Góra/Warsaw Józef Korbicz Jan Maciej Kościelny

7 Contents 1 Introduction... 1 Jan Maciej Kościelny 1.1 ControlSystemStructures Trends in the Development of Modern Automatic Control Systems New Functions of Advanced Automatic Control Systems Introduction to the DiaSter System Jan Maciej Kościelny, Micha l Syfert, Pawe lwnuk 2.1 Introduction SystemStructureandTasks MainUsesoftheSystem SystemFunctionality SystemStructure SoftwarePlatform Information Model and the System Configuration Central Archival Database and User Databases DataExchange ModelingModule On-lineCalculationModule VisualizationModule Process Modeling Krzysztof Janiszowski, Józef Korbicz, Krzysztof Patan, Marcin Witczak 3.1 Introduction AnalyticalModelsandModeling Basic Relations for Balance Dependencies IntegrationMethods... 62

8 X Contents Pneumatic Cylinder: A Balance Model Pneumatic Cylinder: A Block Model Linear Models: Local Approximation of Dynamic Properties DynamicModelLinearization PneumaticCylinder:ALinearModel Pneumatic Cylinder: An Optimized Linear Model ParametricModels DiscreteLinearParametricModels Identification of the Coefficients of Parametric Models Pneumatic Cylinder: A Parametric Linear Model FuzzyParametricModels FuzzyParametricTSK Models Estimation of Fuzzy TSK Model Coefficients Pneumatic Cylinder: A TSK Fuzzy Model NeuralModels NeuralNetworkswithExternalDynamics RecurrentNetworks StateSpaceNeuralNetworks LocallyRecurrentNetworks GMDHNeuralNetworks Implementation of Neural Models in the DiaSter System Knowledge Discovery in Databases Wojciech Moczulski, Robert Szulim, Piotr Tomasik, Dominik Wachla 4.1 Introduction Selection of Input Variables of Models Correlation-Based Feature Selection MeasuresBasedonCorrelation SearchingthroughtheFeatureSpace DiscoveryofQualitativeDependencies DiscoveryofQuantitativeDependencies Support Vector Machines MethodsInvolvingCase-BasedReasoning Conclusion Diagnostic Methods Wojciech Cholewa, Józef Korbicz, Jan Maciej Kościelny, Krzysztof Patan, Tomasz Rogala, Micha l Syfert, Marcin Witczak 5.1 Introduction Specificity of the Diagnostics of Industrial Processes FaultDetectionMethods

9 Contents XI 5.4 RobustFaultDiagnosis Robust Neural Model: The Passive Approach Fuzzy Adaptive Threshold: The Passive Approach Robust Dynamic Model: The Active Approach RobustModelDesignExamples Implementation of Neural Models in the DiaSter System Process Fault Isolation with the Use of Fuzzy Logic Forms of Diagnostic Relation Notation Reasoning Algorithm for Single and Multiple Faults Algorithms of Reasoning in a Hierarchical Structure Application of Belief Networks in Technical Diagnostics Introduction Belief-Network-Based Diagnostic Model Input Data Images AdditionalVariables BeliefNetworks ModelIdentificationandTuning Implementation in the DiaSter Environment Supervisory Control and Optimization Piotr Tatjewski, Leszek Trybus, Maciej Lawryńczuk, Piotr Marusak, Zbigniew Świder, Andrzej Stec 6.1 Predictive Control and Process Set-Point Optimization Principle of Model-Based Predictive Control DynamicMatrixControlAlgorithm Generalized Predictive Control Algorithm Non-linearPredictiveControl OptimizationofSet-Points Examples Self-tuning and Adaptation of Control Loops StepResponseMethod RelaySelf-tuning LoopAdaptation FunctionBlocks Application of the DiaSter System Micha l Syfert, Pawe l Chrzanowski, Bart lomiej Fajdek, Maciej Lawryńczuk, Piotr Marusak, Krzysztof Patan, Tomasz Rogala, Andrzej Stec, Robert Szulim, Piotr Tomasik, Dominik Wachla, Marcin Witczak 7.1 Introduction System of Automatic Control and Diagnostics

10 XII Contents 7.3 Process Information Model in the DiaSter Platform ApplicationsoftheDiaSterSystemPackages ProcessSimulator Self-tuning:SelectionofPIDSettings Reconstructing Process Variables with TSK Models ProcessModelingwithNeuralNetworks IncipientFaultTracking On-Line Diagnostics with Fuzzy Reasoning Belief Networks in a Diagnostic System Knowledge Discovery in Databases ModelPredictiveControlwithConstraints References Index

11 List of Contributors Pawe l Chrzanowski Department of Fundamentals of Machinery Design, Silesian University of Technology, ul. Konarskiego 18A, Gliwice, Poland pawel.chrzanowski@polsl.pl Wojciech Cholewa Department of Fundamentals of Machinery Design, Silesian University of Technology, ul. Konarskiego 18A, Gliwice, Poland wojciech.cholewa@polsl.pl Bart lomiej Fajdek Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, Warsaw, Poland b.fajdek@mchtr.pw.edu.pl Krzysztof Janiszowski Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, Warsaw, Poland kjanisz@mchtr.pw.edu.pl Józef Korbicz Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, Zielona Góra, Poland j.korbicz@issi.uz.zgora.pl Jan Maciej Kościelny Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, Warsaw, Poland jmk@mchtr.pw.edu.pl Maciej Lawryńczuk Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, Poland m.lawrynczuk@ia.pw.edu.pl Piotr Marusak Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, Poland p.marusak@ia.pw.edu.pl

12 XIV Wojciech Moczulski Department of Fundamentals of Machine Design, Silesian University of Technology, ul. Konarskiego 18A, Gliwice, Poland Krzysztof Patan Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, Zielona Góra, Poland Tomasz Rogala Department of Fundamentals of Machinery Design, Silesian University of Technology, ul. Konarskiego 18A, Gliwice, Poland Andrzej Stec Department of Computer and Control Engineering, Rzeszów University of Technology, ul. W. Pola 2, Rzeszów, Poland Micha l Syfert Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, Warsaw, Poland msyfert@mchtr.pw.edu.pl Robert Szulim Institute of Electrical Metrology, University of Zielona Góra, ul. Podgórna 50, Zielona Góra, Poland r.szulim@ime.uz.zgora.pl Zbigniew Świder Department of Computer and Control Engineering, Rzeszów List of Contributors University of Technology, ul. W. Pola 2, Rzeszów, Poland swiderzb@prz-rzeszow.pl Piotr Tatjewski Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, Poland p.tatjewski@ia.pw.edu.pl Piotr Tomasik Department of Fundamentals of Machine Design, Silesian University of Technology, ul. Konarskiego 18A, Gliwice, Poland piotr.tomasik@polsl.pl Leszek Trybus Department of Computer and Control Engineering, Rzeszów University of Technology, ul. W. Pola 2, Rzeszów, Poland ltrybus@prz-rzeszow.pl Dominik Wachla Department of Fundamentals of Machine Design, Silesian University of Technology, ul. Konarskiego 18A, Gliwice, Poland dominik.wachla@polsl.pl Marcin Witczak Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, Zielona Góra, Poland m.witczak@issi.uz.zgora.pl Pawe l Wnuk Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, Warsaw, Poland p.wnuk@mchtr.pw.edu.pl