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

Ensembles and locality : insight on improving software effort estimation

AbstractContextEnsembles of learning machines and locality are considered two important topics for the next research frontier on Software Effort Estimation (SEE).ObjectivesWe aim at (1) evaluating whether existing automated ensembles of learning machines generally improve SEEs given by single learning machines and which of them would be more useful; (2) analysing the adequacy of different locality approaches; and getting insight on (3) how to improve SEE and (4) how to evaluate/choose machine learning (ML) models for SEE.MethodA principled experimental framework is used for the analysis and to provide insights that are not based simply on intuition or speculation. A comprehensive experimental study of several automated ensembles, single learning machines and locality approaches, which present features potentially beneficial for SEE, is performed. Additionally, an analysis of feature selection and regression trees (RTs), and an investigation of two tailored forms of combining ensembles and locality are performed to provide further insight on improving SEE.ResultsBagging ensembles of RTs show to perform well, being highly ranked in terms of performance across different data sets, being frequently among the best approaches for each data set and rarely performing considerably worse than the best approach for any data set. They are recommended over other learning machines should an organisation have no resources to perform experiments to chose a model. Even though RTs have been shown to be more reliable locality approaches, other approaches such as k-Means and k-Nearest Neighbours can also perform well, in particular for more heterogeneous data sets.ConclusionCombining the power of automated ensembles and locality can lead to competitive results in SEE. By analysing such approaches, we provide several insights that can be used by future research in the area

Outlier Detection Methods for Industrial Applications

An outlier is an observation (or measurement) that is different with respect to the other values contained in a given dataset. Outliers can be due to several causes. The measurement can be incorrectly observed, recorded or entered into the process computer, the observed datum can come from a different population with respect to the normal situation and thus is correctly measured but represents a rare event. In literature different definitions of outlier exist: the most commonly referred are reported in the following: - "An outlier is an observation that deviates so much from other observations as to arouse suspicions that is was generated by a different mechanism " (Hawkins, 1980). - "An outlier is an observation (or subset of observations) which appear to be inconsistent with the remainder of the dataset" (Barnet & Lewis, 1994). - "An outlier is an observation that lies outside the overall pattern of a distribution" (Moore and McCabe, 1999). - "Outliers are those data records that do not follow any pattern in an application" (Chen and al., 2002). - "An outlier in a set of data is an observation or a point that is considerably dissimilar or inconsistent with the remainder of the data" (Ramasmawy at al., 2000). Many data mining algorithms try to minimize the influence of outliers for instance on a final model to develop, or to eliminate them in the data pre-processing phase. However, a data miner should be careful when automatically detecting and eliminating outliers because, if the data are correct, their elimination can cause the loss of important hidden information (Kantardzic, 2003). Some data mining applications are focused on outlier detection and they are the essential result of a data-analysis (Sane & Ghatol, 2006). The outlier detection techniques find applications in credit card fraud, network robustness analysis, network intrusion detection, financial applications and marketing (Han & Kamber, 2001). A more exhaustive list of applications that exploit outlier detection is provided below (Hodge, 2004): - Fraud detection: fraudulent applications for credit cards, state benefits or fraudulent usage of credit cards or mobile phones. - Loan application processing: fraudulent applications or potentially problematical customers. - Intrusion detection, such as unauthorized access in computer networks

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Cascade of classifier ensembles for reliable medical image classification

Medical image analysis and recognition is one of the most important tools in modern medicine. Different types of imaging technologies such as X-ray, ultrasonography, biopsy, computed tomography and optical coherence tomography have been widely used in clinical diagnosis for various kinds of diseases. However, in clinical applications, it is usually time consuming to examine an image manually. Moreover, there is always a subjective element related to the pathological examination of an image. This produces the potential risk of a doctor to make a wrong decision. Therefore, an automated technique will provide valuable assistance for physicians. By utilizing techniques from machine learning and image analysis, this thesis aims to construct reliable diagnostic models for medical image data so as to reduce the problems faced by medical experts in image examination. Through supervised learning of the image data, the diagnostic model can be constructed automatically. The process of image examination by human experts is very difficult to simulate, as the knowledge of medical experts is often fuzzy and not easy to be quantified. Therefore, the problem of automatic diagnosis based on images is usually converted to the problem of image classification. For the image classification tasks, using a single classifier is often hard to capture all aspects of image data distributions. Therefore, in this thesis, a classifier ensemble based on random subspace method is proposed to classify microscopic images. The multi-layer perceptrons are used as the base classifiers in the ensemble. Three types of feature extraction methods are selected for microscopic image description. The proposed method was evaluated on two microscopic image sets and showed promising results compared with the state-of-art results. In order to address the classification reliability in biomedical image classification problems, a novel cascade classification system is designed. Two random subspace based classifier ensembles are serially connected in the proposed system. In the first stage of the cascade system, an ensemble of support vector machines are used as the base classifiers. The second stage consists of a neural network classifier ensemble. Using the reject option, the images whose classification results cannot achieve the predefined rejection threshold at the current stage will be passed to the next stage for further consideration. The proposed cascade system was evaluated on a breast cancer biopsy image set and two UCI machine learning datasets, the experimental results showed that the proposed method can achieve high classification reliability and accuracy with small rejection rate. Many computer aided diagnosis systems face the problem of imbalance data. The datasets used for diagnosis are often imbalanced as the number of normal cases is usually larger than the number of the disease cases. Classifiers that generalize over the data are not the most appropriate choice in such an imbalanced situation. To tackle this problem, a novel one-class classifier ensemble is proposed. The Kernel Principle Components are selected as the base classifiers in the ensemble; the base classifiers are trained by different types of image features respectively and then combined using a product combining rule. The proposed one-class classifier ensemble is also embedded into the cascade scheme to improve classification reliability and accuracy. The proposed method was evaluated on two medical image sets. Favorable results were obtained comparing with the state-of-art results

One-Class Subject Identification From Smartphone-Acquired Walking Data

In this work, a novel type of human identification system is proposed, which has the aim to recognize a user from his biometric traits of his way of walk. A smartphone is utilized to acquire motion data from the built-in sensors. Data from accelerometer and gyroscope are processed through a cycle extraction phase, a Convolutional Neural Network for feature extraction and a One-Class SVM classifier for identification. From quantitave results the system achieves an Equal Error Rate close to 1

Nonparametric Bayesian Deep Learning for Scientific Data Analysis

Deep learning (DL) has emerged as the leading paradigm for predictive modeling in a variety of domains, especially those involving large volumes of high-dimensional spatio-temporal data such as images and text. With the rise of big data in scientific and engineering problems, there is now considerable interest in the research and development of DL for scientific applications. The scientific domain, however, poses unique challenges for DL, including special emphasis on interpretability and robustness. In particular, a priority of the Department of Energy (DOE) is the research and development of probabilistic ML methods that are robust to overfitting and offer reliable uncertainty quantification (UQ) on high-dimensional noisy data that is limited in size relative to its complexity. Gaussian processes (GPs) are nonparametric Bayesian models that are naturally robust to overfitting and offer UQ out-of-the-box. Unfortunately, traditional GP methods lack the balance of expressivity and domain-specific inductive bias that is key to the success of DL. Recently, however, a number of approaches have emerged to incorporate the DL paradigm into GP methods, including deep kernel learning (DKL), deep Gaussian processes (DGPs), and neural network Gaussian processes (NNGPs). In this work, we investigate DKL, DGPs, and NNGPs as paradigms for developing robust models for scientific applications. First, we develop DKL for text classification, and apply both DKL and Bayesian neural networks (BNNs) to the problem of classifying cancer pathology reports, with BNNs attaining new state-of-the-art results. Next, we introduce the deep ensemble kernel learning (DEKL) method, which is just as powerful as DKL while admitting easier model parallelism. Finally, we derive a new model called a ``bottleneck NNGP\u27\u27 by unifying the DGP and NNGP paradigms, thus laying the groundwork for a new class of methods for future applications

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design