169 research outputs found
Parametric and nonparametric identification of nonlinearity in structural dynamics
The work described in this thesis is concerned with procedures for the identification
of nonlinearity in structural dynamics. It begins with a diagnostic method which uses
the Hubert transform for detecting nonlinearity and describes the neccessary conditions
for obtaining a valid Hubert transform. The transform is shown to be incapable of
producing a model with predictive power. A method based on the identification of
nonlinear restoring forces is adopted for extracting a nonlinear model. The method is
critically examined; various caveats, modifications and improvements are obtained. The
method is demonstrated on time data obtained from computer simulations. It is shown
that a parameter estimation approach to restoring force identification based on direct
least—squares estimation theory is a fast and accurate procedure. In addition, this
approach allows one to obtain the equations of motion for a multi—degree—of—freedom
system even if the system is only excited at one point.
The data processing methods for the restoring force identification including integration
and differentiation of sampled time data are developed and discussed in some detail.
A comparitive study is made of several of the most well—known least—squares
estimation procedures and the direct least —squares approach is applied to data from
several experiments where it is shown to correctly identify nonlinearity in both single—
and multi—degree--of—freedom systems.
Finally, using both simulated and experimental data, it is shown that the recursive
least—squares algorithm modified by the inclusion of a data forgetting factor can be
used to identify time—dependent structural parameters.Science and Engineering
Research Counci
On topological data analysis for structural dynamics: an introduction to persistent homology
Topological methods can provide a way of proposing new metrics and methods of
scrutinising data, that otherwise may be overlooked. In this work, a method of
quantifying the shape of data, via a topic called topological data analysis
will be introduced. The main tool within topological data analysis (TDA) is
persistent homology. Persistent homology is a method of quantifying the shape
of data over a range of length scales. The required background and a method of
computing persistent homology is briefly discussed in this work. Ideas from
topological data analysis are then used for nonlinear dynamics to analyse some
common attractors, by calculating their embedding dimension, and then to assess
their general topologies. A method will also be proposed, that uses topological
data analysis to determine the optimal delay for a time-delay embedding. TDA
will also be applied to a Z24 Bridge case study in structural health
monitoring, where it will be used to scrutinise different data partitions,
classified by the conditions at which the data were collected. A metric, from
topological data analysis, is used to compare data between the partitions. The
results presented demonstrate that the presence of damage alters the manifold
shape more significantly than the effects present from temperature
uncertainty bounds on higher order frfs from gaussian process narx models
One of the most versatile and powerful algorithms for the identification of nonlinear dynamical systems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current output of a system by a nonlinear regression on past inputs and outputs and can also incorporate a nonlinear noise model in the most general case. In recent papers, one of the authors introduced a NARX (no noise model) formulation based on Gaussian Process (GP) regression and derived the corresponding expressions for Higher-order Frequency Response Functions (HFRFs). This paper extends the theory for the GP-NARX framework by providing a means of converting the GP prediction bounds in the time domain into bounds on the HFRFs. The approach is demonstrated on the Duffing oscillator
Experimental studies on impact damage location in composite aerospace structures using genetic algorithms and neural networks
Impact damage detection in composite structures has gained a considerable interest in many engineering
areas. The capability to detect damage at the early stages reduces any risk of catastrophic failure. This paper
compares two advanced signal processing methods for impact location in composite aircraft structures. The first
method is based on a modified triangulation procedure and Genetic Algorithms whereas the second technique
applies Artificial Neural Networks. A series of impacts is performed experimentally on a composite aircraft wing�box structure instrumented with low-profile, bonded piezoceramic sensors. The strain data are used for learning in
the Neural Network approach. The triangulation procedure utilises the same data to establish impact velocities for
various angles of strain wave propagation. The study demonstrates that both approaches are capable of good
impact location estimates in this complex structure
Towards risk-informed PBSHM: Populations as hierarchical systems
The prospect of informed and optimal decision-making regarding the operation
and maintenance (O&M) of structures provides impetus to the development of
structural health monitoring (SHM) systems. A probabilistic risk-based
framework for decision-making has already been proposed. However, in order to
learn the statistical models necessary for decision-making, measured data from
the structure of interest are required. Unfortunately, these data are seldom
available across the range of environmental and operational conditions
necessary to ensure good generalisation of the model.
Recently, technologies have been developed that overcome this challenge, by
extending SHM to populations of structures, such that valuable knowledge may be
transferred between instances of structures that are sufficiently similar. This
new approach is termed population-based structural heath monitoring (PBSHM).
The current paper presents a formal representation of populations of
structures, such that risk-based decision processes may be specified within
them. The population-based representation is an extension to the hierarchical
representation of a structure used within the probabilistic risk-based decision
framework to define fault trees. The result is a series, consisting of systems
of systems ranging from the individual component level up to an inventory of
heterogeneous populations. The current paper considers an inventory of wind
farms as a motivating example and highlights the inferences and decisions that
can be made within the hierarchical representation.Comment: Submitted to IMAC-XLI conference (2023), Austin, Texas, US
Simulation of ultrasonic lamb wave generation, propagation and detection for an air coupled robotic scanner
A computer simulator, to facilitate the design and assessment of a reconfigurable, air-coupled ultrasonic scanner is described and evaluated. The specific scanning system comprises a team of remote sensing agents, in the form of miniature robotic platforms that can reposition non-contact Lamb wave transducers over a plate type of structure, for the purpose of non-destructive evaluation (NDE). The overall objective is to implement reconfigurable array scanning, where transmission and reception are facilitated by different sensing agents which can be organised in a variety of pulse-echo and pitch-catch configurations, with guided waves used to generate data in the form of 2-D and 3-D images. The ability to reconfigure the scanner adaptively requires an understanding of the ultrasonic wave generation, its propagation and interaction with potential defects and boundaries. Transducer behaviour has been simulated using a linear systems approximation, with wave propagation in the structure modelled using the local interaction simulation approach (LISA). Integration of the linear systems and LISA approaches are validated for use in Lamb wave scanning by comparison with both analytic techniques and more computationally intensive commercial finite element/difference codes. Starting with fundamental dispersion data, the paper goes on to describe the simulation of wave propagation and the subsequent interaction with artificial defects and plate boundaries, before presenting a theoretical image obtained from a team of sensing agents based on the current generation of sensors and instrumentation
Sensor Validation for On-line Vibration Monitoring
For a reliable on-line vibration monitoring of structures, it is necessary to have accurate sensor information. However, sensors may sometimes be faulty or may even become unavailable due to failure or maintenance activities. The problem of sensor validation is therefore a critical part or structural identification. The objective of the present study is to present a procedure based on principal component analysis, which is able to perform detection, isolation and reconstruction of a faulty sensor. Its e ciency is assessed using an experimental application
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