25 research outputs found
A machine learning approach to Structural Health Monitoring with a view towards wind turbines
The work of this thesis is centred around Structural Health Monitoring (SHM) and
is divided into three main parts.
The thesis starts by exploring di�erent architectures of auto-association. These are
evaluated in order to demonstrate the ability of nonlinear auto-association of neural
networks with one nonlinear hidden layer as it is of great interest in terms of reduced
computational complexity. It is shown that linear PCA lacks performance for novelty
detection. The novel key study which is revealed ampli�es that single hidden layer
auto-associators are not performing in a similar fashion to PCA.
The second part of this study concerns formulating pattern recognition algorithms for
SHM purposes which could be used in the wind energy sector as SHM regarding this
research �eld is still in an embryonic level compared to civil and aerospace engineering.
The purpose of this part is to investigate the e�ectiveness and performance of such
methods in structural damage detection. Experimental measurements such as high
frequency responses functions (FRFs) were extracted from a 9m WT blade throughout
a full-scale continuous fatigue test. A preliminary analysis of a model regression of
virtual SCADA data from an o�shore wind farm is also proposed using Gaussian
processes and neural network regression techniques.
The third part of this work introduces robust multivariate statistical methods into
SHM by inclusively revealing how the in
uence of environmental and operational
variation a�ects features that are sensitive to damage. The algorithms that are
described are the Minimum Covariance Determinant Estimator (MCD) and the Minimum Volume Enclosing Ellipsoid (MVEE). These robust outlier methods are
inclusive and in turn there is no need to pre-determine an undamaged condition
data set, o�ering an important advantage over other multivariate methodologies.
Two real life experimental applications to the Z24 bridge and to an aircraft wing
are analysed. Furthermore, with the usage of the robust measures, the data variable
correlation reveals linear or nonlinear connections
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
A decision framework for selecting information-transfer strategies in population-based SHM
Decision-support for the operation and maintenance of structures provides
significant motivation for the development and implementation of structural
health monitoring (SHM) systems. Unfortunately, the limited availability of
labelled training data hinders the development of the statistical models on
which these decision-support systems rely. Population-based SHM seeks to
mitigate the impact of data scarcity by using transfer learning techniques to
share information between individual structures within a population. The
current paper proposes a decision framework for selecting transfer strategies
based upon a novel concept -- the expected value of information transfer --
such that negative transfer is avoided. By avoiding negative transfer, and by
optimising information transfer strategies using the transfer-decision
framework, one can reduce the costs associated with operating and maintaining
structures, and improve safety.Comment: 12 pages, 2 figures. Author accepted manuscript in Proceedings of the
14th International Workshop on Structural Health Monitoring, Stanford
University, California, USA. 202
ABC-NS: a new computational inference method applied to parameter estimation and model selection in structural dynamics
The inference of dynamical systems is a challenging issue, particularly when the dynamics include complex phenomena such as the existence of bifurcations and/or chaos. In this situation, the likelihood function formulated based on time-series data may be complex with several local minima and as a result not suitable for parameter inference. In the most challenging scenarios, the likelihood function may not be available in an analytical form, so a standard statistical inference is impossible to carry out. To overcome this problem, the inclusion of new features/invariants less sensitive to small variations from either the time or frequency domains seems to be potentially a very useful way to make Bayesian inference. The use of approximate Bayesian computation (ABC) or likelihood-free algorithms is an appropriate option as they offer the flexibility to use different metrics for parameter inference. However, most variants of the ABC algorithm are inefficient due to the low acceptance rate. In this contribution, a new ABC algorithm based on an ellipsoidal nested sampling technique is proposed to overcome this issue. It will be shown that the new algorithm performs perfectly well and maintains a relatively high acceptance rate through the iterative inference process. In addition to parameter estimation, the new algorithm allows one to deal with the model selection issue. To demonstrate its efficiency and robustness, a numerical example is presented
Multitask feature selection within structural datasets
Population-based structural health monitoring (PBSHM) systems use data from multiple structures to make inferences of health states. An area of PBSHM that has recently been recognized for potential development is the use of multitask learning (MTL) algorithms that differ from traditional single-task learning. This study presents an application of the MTL approach, Joint Feature Selection with LASSO, to provide automatic feature selection. The algorithm is applied to two structural datasets. The first dataset covers a binary classification between the port and starboard side of an aircraft tailplane, for samples from two aircraft of the same model. The second dataset covers normal and damaged conditions for pre- and postrepair of the same aircraft wing. Both case studies demonstrate that the MTL results are interpretable, highlighting features that relate to structural differences by considering the patterns shared between tasks. This is opposed to single-task learning, which improved accuracy at the cost of interpretability and selected features, which failed to generalize in previously unobserved experiments
A sampling-based approach for information-theoretic inspection management
A partially supervised approach to Structural Health Monitoring is proposed, to manage the cost associated with expert inspections and maximize the value of monitoring regimes. Unlike conventional data-driven procedures, the monitoring classifier is learnt online while making predictions—negating the requirement for complete data before a system is in operation (which are rarely available). Most critically, periodic inspections are replaced (or enhanced) by an automatic inspection regime, which only queries measurements that appear informative to the evolving model of the damage-sensitive features. The result is a partially supervised Dirichlet process clustering that manages expert inspections online given incremental data. The method is verified on a simulated example and demonstrated on in situ bridge monitoring data
Decomposition of multi-mode signals using dispersion curves and Bayesian linear regression
For certain structure types and damage sizes, guided waves offer some distinct advantages for damage detection, such as range and sizing potential, greater sensitivity and cost effectiveness. Guided waves exhibit multiple modes; for Lamb waves there are two types; symmetric and antisymmetric. In damage detection regimes, information and features of individual modes, which propagate from a single source, are useful for localisation and sizing of damage. This facet leads to the motivation to decompose a single signal into the individual modes that are received in the wave-packet. Decomposition of wave modes is possible in full-field Lamb wave data from a forward-backward, two-dimensional Fourier transform method that involves dispersion curve information; though this method cannot be applied directly to signals at a single location. By using this method, the expected nominal waves can be determined for a given propagation distance; i.e. the individual wave modes expected to be present regardless of damage. In the presence of damage, residual signals will be present which contain information on the damage. In this paper, a Bayesian linear regression technique is used to decompose single multi-mode signals into their individual wave modes, which is then used to determine any residual signals. This decomposition is made by determining the expected shape and size of individual mode signals from the full-field decomposed waves. The information inferred by this method, both before and after the wave has propagated through damage, is studied
Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems
To maximize its value, the design, development and implementation of
Structural Health Monitoring (SHM) should focus on its role in facilitating
decision support. In this position paper, we offer perspectives on the synergy
between SHM and decision-making. We propose a classification of SHM use cases
aligning with various dimensions that are closely linked to the respective
decision contexts. The types of decisions that have to be supported by the SHM
system within these settings are discussed along with the corresponding
challenges. We provide an overview of different classes of models that are
required for integrating SHM in the decision-making process to support
management and operation and maintenance of structures and infrastructure
systems. Fundamental decision-theoretic principles and state-of-the-art methods
for optimizing maintenance and operational decision-making under uncertainty
are briefly discussed. Finally, we offer a viewpoint on the appropriate course
of action for quantifying, validating and maximizing the added value generated
by SHM. This work aspires to synthesize the different perspectives of the SHM,
Prognostic Health Management (PHM), and reliability communities, and deliver a
roadmap towards monitoring-based decision support
Model selection and parameter estimation in structural dynamics using approximate Bayesian computation
This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours