Structural Health Monitoring Using Genetic Fuzzy Systems

Transcription

1 Structural Health Monitoring Using Genetic Fuzzy Systems

2 Prashant M. Pawar Ranjan Ganguli Structural Health Monitoring Using Genetic Fuzzy Systems

3 Prashant M. Pawar College of Engineering Shri Vithal Education and Research Institute Pandharpur India Ranjan Ganguli Department of Aerospace Engineering Indian Institute of Science Bangalore India Additional material to this book can be downloaded from ISBN e-isbn DOI / Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: Springer-Verlag London Limited 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of 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 laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: VTeX UAB, Lithuania Printed on acid-free paper Springer is part of Springer Science+Business Media (

4 Preface Structural health monitoring (SHM) has emerged as an important research area in recent years because of its strong links with structural safety and the need to monitor and extend the lives of existing structures. SHM is an interdisciplinary field, combining elements of mechanics with those of information science and sensors and actuators. The practical importance of SHM is clear from the continuing failures which affect engineering structures such as bridges, aircraft, helicopters, and nuclear reactors. In many cases, a health monitoring system installed on the structure can detect and isolate the damage before it becomes catastrophic, thereby reducing the likelihood of failures. SHM systems can therefore reduce costs and save lives. A key problem in SHM involves performing damage detection and isolation from a set of measured data. Typically, the measured data is contaminated with noise, and the number of measurements is limited. In model-based SHM, a mathematical model is used to develop simulated measured data for the damaged structure. Then, the simulated data is used to develop a pattern recognition approach which maps the damage location and size to the simulated data. Algorithms such as neural networks are often used to perform this pattern recognition task. However, neural networks tend to be black boxes which are difficult to understand. In this book, an alternative and powerful architecture, the genetic fuzzy system (GFS), is demonstrated for beams, composite tubes, and helicopter rotor blade health monitoring. A novel feature of this book is the focus on helicopter rotor health monitoring, as this represents a system of considerable complexity. The fuzzy logic approaches addresses uncertainty directly through the linguistic fuzzifier and is very well suited to SHM because it gives linguistic outputs which can be used to guide prognostic action. The use of the genetic algorithm automates the development of the fuzzy system and makes the method easy to use for problems involving a large number of measurements and damage location sizes, which is typical of SHM. By demonstrating the use of the GFS as a series of progressively complicated structures, this book enables the reader to learn about this new and powerful approach to SHM. The book also provides some MATLAB code for the v

5 vi Preface algorithms developed. This book will be useful for aerospace, civil, and mechanical engineers working in the area of SHM. It will also be useful for computer scientists and applied mathematicians interested in the application of GFSs to engineering problems. Bangalore, India Prashant M. Pawar Ranjan Ganguli

6 Contents 1 Introduction TermsandDefinitionsRelatedtoSHM SHM Approaches Model-Based SHM Modal-Based Methods Localized Methods Soft Computing Methods for Health Monitoring NeuralNetworks GeneticAlgorithms Fuzzy Logic System Hybridized Soft Computing Methods BookSummary References Genetic Fuzzy System Fuzzy Logic System GeneticAlgorithms Operations During a GA Process Performance Factors Genetic Fuzzy System Summary References Structural Health Monitoring of Beams DamageModelingin1DBeam Formulation of Genetic Fuzzy System StructuralHealthMonitoringforUniformBeam TestwithNoisyData TestwithDifferentMeasurements MissingandFaultyMeasurements MeasurementswithHighNoise vii

7 viii Contents 3.4 Structural Health Monitoring for Nonuniform Beam Refined Output Set SHM for BO105 Hingeless Helicopter Rotor Blade Frequency-Based Damage Detection of Blade Mode Shape-Based Damage Detection of Blade Summary References Structural Health Monitoring of Composite Tubes Matrix Cracking in Hollow Circular Cross Section Effective Elastic Modulus Matrix Crack Model ([±θ m /90 n ] Family ofcomposites) Modal Analysis DamageDetectioninCompositeStructure Development of Genetic Fuzzy System Testing of Genetic Fuzzy System AnalysisofMisclassification Summary References Structural Health Monitoring of Composite Helicopter Rotor Mathematical Model Mathematical Model of Helicopter Rotor CompositeRotorBlade Progressive Damage Accumulation Data Reduction LifeoftheStructure BehaviorofCompositeRotorBlade Effect on Cross-Sectional Stiffness Effect on Static Response NumericalSimulationofMeasurementDeltas Blade Tip Response Blade Root Loads ThresholdsBasedonMatrixCrackSaturation Strains PredictingLifeConsumption Development of Genetic Fuzzy System Testing of Genetic Fuzzy System ImplementationoftheSHMSystem Summary References Appendix MATLAB Codes Index...129