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

Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data

Outlier ensembles are shown to provide a robust method for damage detection and dimension reduction via a wholly unsupervised framework. Most interestingly, when utilised for feature extraction, the proposed heuristic defines features that enable near-equivalent classification performance (95.85%) when compared to the features found (in previous work) through supervised techniques (97.39%) — specifically, a genetic algorithm. This is significant for practical applications of structural health monitoring, where labelled data are rarely available during data mining. Ensemble analysis is applied to practical examples of problematic engineering data; two case studies are presented in this work. Case study I illustrates how outlier ensembles can be used to expose outliers hidden within a dataset. Case study II demonstrates how ensembles can be utilised as a tool for robust outlier analysis and feature extraction in a noisy, high-dimensional feature-space

Robust methods for outlier detection and regression for SHM applications.

In this paper, robust statistical methods are presented for the data-based approach to structural health monitoring (SHM). The discussion initially focuses on the high level removal of the ‘masking effect’ of inclusive outliers. Multiple outliers commonly occur when novelty detection in the form of unsupervised learning is utilised as a means of damage diagnosis; then benign variations in the operating or environmental conditions of the structure must be handled very carefully, as it is possible that they can lead to false alarms. It is shown that recent developments in the field of robust regression can provide a means of exploring and visualising SHM data as a tool for exploring the different characteristics of outliers, and removing the effects of benign variations. The paper is not, in any sense, a survey; it is an overview and summary of recent work by the authors

A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions

Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.Peer ReviewedPostprint (published version

Detection of Malaria by Multispectral Microscopy using Statistical Classification Methods

There has been much work on classification of malaria infected blood; in resent time, a method using LED-based microscopy has been developed with the goal of reducing time and cost. The education level needed to make such decisions is also reduced using this microscope. This is mainly done to help the developing countries in the fight against malaria and develop these countries competence in the field of multispectral analysis. The LED-microscope used, was constructed during a workshop including scientists from Lund and 6 developing countries in Africa, so there is identical equipment in the field in these countries. This could be a useful complement to the pathologists in the field. The LED- microscope uses 13 different wavelengths and 3 ways to illuminate the sample (reflecting, scattering and transmitting). Each combination is used, all in all 39 different pictures of the sample. An automatic process based on this would be a great help to simplify the detection of malaria. The goal of this thesis is to analyze different methods of classifying malaria using data from the LED-microscope. The data was collected by already existing, and under development, methods using the LED-microscope. The main two statistical methods used in this thesis to do the classification are: First Fisher’s Linear discriminant to reduce the dimensions with minimal information loss. Second Ellipsoidal constraints to formulate an optimization problem that is then rewritten as a convex optimization problem, which is then solved. Then the classification is analyzed to conclude if the samples contain malaria, and to find suitable thresholds for the classifiers, every analyzed method is evaluated. Even when this is mainly software optimization it can impact the work needed to construct new microscopes to make it more efficient. Also contained in this thesis are some examples from analysis of both fresh and old malaria-infected blood samples to see the difference between the different methods. The methods is quite general and can with little extra work be applied to different data sets, when the microscope is used to gather data from other sources than blood samples

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.La versión final de este artículo fue publicada el 29 de junio de 2023 en Brain Topography (Springer). Se encuentra accesible desde Biblioteca Di Tella a través de Prim