Application of System Identification in Engineering

577 Pages·1988·32.729 MB·English

by  H. G. Natke (eds.)

INTERNATIONAL CENTRE FOR MECHANICAL SCIENCES
COURSES AND LECTURES - No. 296
APPLICATION OF SYSTEM
IDENTIFICATION IN ENGINEERING
EDITED BY
H.G. NATKE
UNIVERSITÄT HANNOVER
SPRINGER-VERLAG WIEN GMBH
l.e spese cli stampa cli questo volume sono in parte coperte da contributi
del Cbnslglio Nazionale delle Ricerche.
This volume contalns196 Wustrations.
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© 1988 by Springer-Verlag Wien
Originally publisbed by Springer Verlag Wien-New York in 1988
ISBN 978-3-211-82052-0 ISBN 978-3-7091-2628-8 (eBook)
DOI 10.1007/978-3-7091-2628-8
PREFACE
System identification is a powerful tool in Engineering. lts various methods in thefrequency
andin the time domain have been extensively discussed in earlier CISM courses. The aim of
this course is to describe the state of the art in specific application areas, e.g. estimation of
eigenquantities (in the airplane and aerospace industry, in civil engineering, in naval
engineering etc.), noise source detection, fault detection by investigation of dynamic
properties, such as machine sound characteristics, and the identification of the dynamic
behaviour ofj low induced systems (e.g. aeroelastic problems). Geotechnical app/ications are
also one of the fields of interest.
The lecture notes contain demonstrations of several methods and inc/ude a valuation of
combining various kinds of experience. Such complex information inc/udes not only
theoretical aspects of identification but also advice on practical handling, e.g. concerning
testing effort and data handling.
The course was announced as "The Boltzmann Session·: Boltzmann, who was an excellent
theoretical physicist as weil as a very ski/ful experimenter, once said that "nothing is more
practical than theory". I entirely agree with him. The readerwill thusfind an introductory
review of the identification of vibrating structures, and, of course, some more theoretically
orientated papers.
I wish to thank all the participants in the course for their contributions, especially the
lecturers for the oustanding work they did in Udine andf or preparing their final papers. lAst
but not least our thanks must go to the CISM cooperatorsfor the excellent work they have
done and for their kind hospitality.
H.G. Natke
Hannover.
CONTENTS
Page
Preface
Introduction to System ldentification: Fundamentals and Survey
by H.G. Natke and N. Cottin •••••••.••••.•.••.....••.•...•.•••.••••••.••.•.•.••. 3
Balanced State Space Representation in the ldentification of Dynamical Systems
by W. Gawronski and H. G. Natke •.•..•.•••.•••••••••.••••.•..•.••.•..•••...••• III
Non-Linearity in Dynamical Systems
by G.R. Tomlinson .••..••.•..•••.•.••..••.•••.•..•.••••••.•.••••••••••.•••.•• 199
Numerical Acoustic Radiation Models
by P. Sas ••.•.•.••••.••.•••...••.•.•.•.•••....••.•••.•..•.•••.•.•.••.•••.•••• 233
Digital Acoustic Intensity Measurements
by P. Sas ••.•..••.•.•.•.•..••••.•••.•••••.•••..••••.••••••.•.•••••••.••.••••. 251
Multiple Input/Output Analysis: an ldentification Tool in Acoustics
by P. Sas ••••.•.••••••.•..•..•...••..••....•••••••.•.••••..••••....•.•••.•.•• 279
Structural Identification on Nonlinear Systems Subjected to Quasistatic Loading
by A. Nappi ••••.••.•.•....••.•••.•..••.•••..••••••••.•..•.••.•••.•••••••••.• 293
Applications in Civil Engineering: Modal Parameter Identification of an Offshore
Platform and ldentification of the Areodynamic Admittance Functions of Tall
Buildings
by H. G. Natke ••••.••.••..•...•.••.••••....•••..••.•....•.•...•••.•..••.....• 325
Identification of Structural Darnage in Civil Engineering
by J.T.P. Yao •••.•..••.••••.•••••..•••....•.•••.....•••••......•.••••••.•.•.. 349
Applications in Areospace and Airplane Engineering: Estimation of Modal
Quantities and Model Improvement
by H.G. Natke ••••••.••••••••.•••••.••••.•.••••••.•••.••••.••••••••••••••.••• 391
Aircraft System ldentification - Determination of Flight Mechanics Parameters
by E. Plaetschke and S. Weiss .•••••••••••••••.••••••.••••••.•.••••.•••••••••.•• 421
Application of System ldentification in Naval Engineering
by R. Maltese et al. .••••••••.•••.••••••••••••••.••••••••.•.••••••••••••••••••• 449
Determination of Frequency Responses by Means of Pseudorandom Signals
by A. Lingener •••••••••••••.••••••.•••••••••••••••••••••••••••••••.•••••••••• 483
2 Contents
Application of Modal Analysis to Linear Elastic Mechanical Systems by the
Example of a Ribbed Plate
by A. Lingener ............................................................... 499
Estimation of Dynamic Spring and Damping Parameters of the Supports of a
Nuclear Reactor by Means of an Adaptive Method in Time Domain
by A. Lingener and S. Doege ................................................... 511
Optimization of a Reduced Torsional Model using a Parameter ldentification
Procerlure
by P. Schwibinger and R. Nordmann ........................................... 525
ldentification of Stiffness, Damping and lnertia Coefficinets of Annular
Turbulent Seals
by R. Nordmann ............................................................. 543
Identification of Modal Parameters of an Elastic Rotor with Oil Film Hearings
by R. Nordmann .............................................................. 565
INTRODUCTION TO SYSTEM IDENTIFICATION:
FUNDAMENTALS AND SURVEY
H.G. Natke, N. Cottla
UniYenltlt Haaao••, ßaaaoy•, F.R.G.
1. Fundamentals
The application of system identification to engineer-
ing problems requires certain knowledge of
- the inherent theoretical relations
- the test and measuring conditions (and their inevitably
imperfect realization)
- the deterministic and statistical approaches in system
identification (e.g. time series analysis).
The content of CISM course 272 in 1980 on "Identifi-
cation of Vibrating Structures" /1.1/ and the existing
refs. dealing wi th time series /1.2, 1.3/, time series and
4 H.G. Natke -N. Cottin
stochastics /1.4/, and such books as contain time series
and experimental modal analysis /1.5,1.6/, mean that the
fundamentals of system identification only need to be sum
marized here. The term "system" is used as a synonym and
abbreviation for mechanical systems /1.7/ to which this
course is restricted.
1.1 General
The systems we are dealing with are assumed to be
time invariant and, in addition, linear and nonlinear. The
dynamic behaviour of the system, and the dynamic process
the system is subjected to, can often be described by
input/ output relations, which result in a system of equa
tions: the mathematical model. In general the goal of
system (structure) analysis is the prediction of the dyna
mic behaviour of the system under investigation. This is
the well-known direct problem of system analysis that re
quires sufficiently accurate system modelling and the
knowledge of dynamic loads. On the other band, the inverse
problem (Fig. 1.1) deale with the modelling and the design
of the system itself (system identification, design pro
blem) and with input identification (Fig. 1.2). In the
design problem, the input and output quantities are given
and one is looking for a system (for its model) which
lntroduction 5
fulfill8 the input/output relation8 be8t. Input identifi-
c a t'i o n i 8 d e f in e d b y a g i v e n mo d e 1 d e s c r i p t i o n an d a g i v e n
output, while 8ystem identification includes the determi-
nation of a system description by measured input and/or
output quantities.
FIG. 1.1 CLASSIFICATION OF
SYSTEM ANALYSIS
As is known, we di8tinguish between the black-box
model and the parametric model (Fig. 1.3). The black-box
model is a non-structured mathematical model, e.g. the
frequency reeponee function. Parametrie system identifica-
tion uses a structured model, 80 that only it8 parameters
are unknown, i.e. the identification problem is reduced to
parameter e8timation.
FIG.1.2 CLASSIFICATION OF THE
INVERSE PROBLEM
6 H.G. Natke-N. Cottin
NON -PARAMETRie PARAMETRie
STRUeTURED MATH.
BLAeK -BOX MODEL
MODEL
FIG.1.3 eLASSIFIGATION OF SI
eoNeERNING THE MATH.
MODEL USED
"Estimation" is used in its mathematical sense: sta-
tistical methods have to be applied, because the measure-
ments are often distorted by random errors which have to
be reduced in order to obtain results with a large infor-
ma tion con ten t and wi th high confidence. The identifica-
tion problem is accompanied by the following practical and
theoretical problems: test conditions (cf. Fig. 1.4) con-
cerning
- test environment and test equipment
- excitation
- measurement techniques
- data acquisition
- signal processing including data reduction
- noise reduction,
considerations concerning
- the choice of mathematical model
- the choice of appropriate estimation methods
- data processing (including algorithms and routines).