Support vector machine based classification in condition monitoring of induction motors

Continuous and trouble-free operation of induction motors is an essential part of modern power and production plants. Faults and failures of electrical machinery may cause remarkable economical losses but also highly dangerous situations. In addition to analytical and knowledge-based models, application of data-based models has established a firm position in the induction motor fault diagnostics during the last decade. For example, pattern recognition with Neural Networks (NN) is widely studied. Support Vector Machine (SVM) is a novel machine learning method introduced in early 90's. It is based on the statistical learning theory presented by V.N. Vapnik, and it has been successfully applied to numerous classification and pattern recognition problems such as text categorization, image recognition and bioinformatics. SVM based classifier is built to minimize the structural misclassification risk, whereas conventional classification techniques often apply minimization of the empirical risk. Therefore, SVM is claimed to lead enhanced generalisation properties. Further, application of SVM results in the global solution for a classification problem. Thirdly, SVM based classification is attractive, because its efficiency does not directly depend on the dimension of classified entities. This property is very useful in fault diagnostics, because the number of fault classification features does not have to be drastically limited. However, SVM has not yet been widely studied in the area of fault diagnostics. Specifically, in the condition monitoring of induction motor, it does not seem to have been considered before this research. In this thesis, a SVM based classification scheme is designed for different tasks in induction motor fault diagnostics and for partial discharge analysis of insulation condition monitoring. Several variables are compared as fault indicators, and forces on rotor are found to be important in fault detection instead of motor current that is currently widely studied. The measurement of forces is difficult, but easily measurable vibrations are directly related to the forces. Hence, vibration monitoring is considered in more detail as the medium for the motor fault diagnostics. SVM classifiers are essentially 2-class classifiers. In addition to the induction motor fault diagnostics, the results of this thesis cover various methods for coupling SVMs for carrying out a multi-class classification problem.reviewe

A VISION-BASED QUALITY INSPECTION SYSTEM FOR FABRIC DEFECT DETECTION AND CLASSIFICATION

Published ThesisQuality inspection of textile products is an important issue for fabric manufacturers. It is desirable to produce the highest quality goods in the shortest amount of time possible. Fabric faults or defects are responsible for nearly 85% of the defects found by the garment industry. Manufacturers recover only 45 to 65% of their profits from second or off-quality goods. There is a need for reliable automated woven fabric inspection methods in the textile industry. Numerous methods have been proposed for detecting defects in textile. The methods are generally grouped into three main categories according to the techniques they use for texture feature extraction, namely statistical approaches, spectral approaches and model-based approaches. In this thesis, we study one method from each category and propose their combinations in order to get improved fabric defect detection and classification accuracy. The three chosen methods are the grey level co-occurrence matrix (GLCM) from the statistical category, the wavelet transform from the spectral category and the Markov random field (MRF) from the model-based category. We identify the most effective texture features for each of those methods and for different fabric types in order to combine them. Using GLCM, we identify the optimal number of features, the optimal quantisation level of the original image and the optimal intersample distance to use. We identify the optimal GLCM features for different types of fabrics and for three different classifiers. Using the wavelet transform, we compare the defect detection and classification performance of features derived from the undecimated discrete wavelet and those derived from the dual-tree complex wavelet transform. We identify the best features for different types of fabrics. Using the Markov random field, we study the performance for fabric defect detection and classification of features derived from different models of Gaussian Markov random fields of order from 1 through 9. For each fabric type we identify the best model order. Finally, we propose three combination schemes of the best features identified from the three methods and study their fabric detection and classification performance. They lead generally to improved performance as compared to the individual methods, but two of them need further improvement

Detecção e caracterização de defeitos internos por termografia infravermelha pulsada

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2014.A termografia pulsada (TP) é uma técnica promissora para a avaliação não-destrutiva de materiais. O protocolo de inspeção consiste na aplicação de um pulso térmico no espécime e monitorar a resposta térmica da superfície via radiação infravermelha (IV). Descontinuidades internas aparecem na sequência térmica como "hot spots" ou padrões térmicos irregulares, os quais são prodruzidos por alterações na taxa interna de difusão de calor. Apesar de ser uma das técnicas mais usadas e atrativas para a avaliação não-destrutiva, sua aplicação apresenta grandes desafios especialmente durante a inspeção de materiais anisotrópicos. 'Blurring', a perda de visibilidade devido aos efeitos da condução lateral de calor e a não-uniformidade produzida durante a excitação térmica representam as maiores limitações da TP. Esta tese é focada na otimização da inspeção por TP em laminados compósitos. Para tal propósito, foi desenvolvido um modelo termo-numérico para a análise da resposta térmica da material devido a presença de defeitos internos. Um estudo paramétrico foi desenvolvido com o objetivo de estudar o impacto do aquecimento não-uniforme, da intensidade da radiação e da geometria dos defeitos em vários parâmetros informativos da inspeção por TP. Uma análise das três técnicas mais usadas para o tratamento de sinais termográficos foi realizada e os seus desempenhos foram avaliados em função da relação sinal-ruído no ponto de maior contraste entre região com defeito e região sem defeito. Neste trabalho foi desenvolvida uma nova técnica de processamento e análise de imagens térmicas. A nova técnica - baseada no método de regressão dos mínimos quadrados parciais (PLSR) - decompõe a sequência térmica em variáveis latentes, permitindo assim a separação das diversas fontes de ruído que afetam a qualidade das imagens. A partir deste método de correlação foi desenvolvido um modelo empírico para a quantificação da profundidade e tamanho lateral dos defeitos empregando dados experimentais. Ambos os métodos - de tratamento de sinais e quantificação de defeitos - foram analisados e comparados com técnicas tradicionais, apresentando uma melhoria substancial na relação sinal-ruído e na precisão no processo de inversão de profundidade e forma dos defeitos.Abstract: Pulsed thermography (PT) is a novel and promissory technique for the nondestructive and evaluation (NDT&E) of materials. The inspection protocol consists in pulse heating the specimen while monitoring the resulting thermal response via infrared (IR) radiation. Subsurface discontinuities appear as transient hot spots or irregular thermal patterns in the thermogram sequence, which are produced by the alterations in the internal heat diffusion fluxes. In spite of being one of the most used and attractive methods for the NDT&E, its application still presents challenges specially when inspecting anisotropic materials. 'Blurring', the lost of defect visibility due to the effects of lateral heat conduction and the non-uniform heating produced during the application of the thermal excitation, represent the major drawbacks of PT. This thesis is focused on the optimization of the PT inspection of laminated composites. A thermal-numerical model is developed in order to analysis the thermal response of the material due to the presence of subsurface defects. A parametric study was performed aiming to study the impact of the effects of non-uniform heating, irradiation density and defects geometry on several informative variables of the PT inspection. An in-depth analysis of three of the most used PT sinal processing techniques was carried out and their performance was evaluated in terms of the signal-to-noise (SNR) at maximum signal contrast. In this work was also developed a new promissory technique for the processing and analysis of thermographic data. The new method - based on partial least squares regression (PLSR) - decomposes the thermal sequence into latent variables, allowing to separate several sources of noise affecting the quality of the images. From the statistical correlation method an empirical model was developed for the quantification of the depth and lateral size of defects using experimental data. Both methods - for the signal processing and for the inversion of depth and lateral size - were analyzed and compared with traditional techniques, achieving a substantial improvement in the signal-to-noise ratio and in the accuracy in the prediction results of depth and lateral size of the defects

A mesoscopic model for the rheology of soft amorphous solids, with application to mi- crochannel flows

We study a mesoscopic model for the flow of amorphous solids. The model is based on the key features identified at the microscopic level, namely peri- ods of elastic deformation interspersed with localised rearrangements of parti- cles that induce long-range elastic deformation. These long-range deformations are derived following a continuum mechanics approach, in the presence of solid boundaries, and are included in full in the model. Indeed, they mediate spatial cooperativity in the flow, whereby a localised rearrangement may lead a distant region to yield. In particular, we simulate a channel flow and find manifestations of spatial cooperativity that are consistent with published experimental obser- vations for concentrated emulsions in microchannels. Two categories of effects are distinguished. On the one hand, the coupling of regions subject to different shear rates, for instance,leads to finite shear rate fluctuations in the seemingly un- sheared "plug" in the centre of the channel. On the other hand, there is convinc- ing experimental evidence of a specific rheology near rough walls. We discuss diverse possible physical origins for this effect, and we suggest that it may be associated with the bumps of particles into surface asperities as they slide along the wall

Analysis of first pass myocardial perfusion imaging with magnetic resonance

Early diagnosis and localisation of myocardial perfusion defects is an important step in the treatment of coronary artery disease. Thus far, coronary angiography is the conventional standard investigation for patients with known or suspected coronary artery disease and it provides information about the presence and location of coronary stenoses. In recent years, the development of myocardial perfusion CMR has extended the role of MR in the evaluation of ischaemic heart disease beyond the situations where there have already been gross myocardial changes such as acute infarction or scarring. The ability to non-invasively evaluate cardiac perfusion abnormalities before pathologic effects occur, or as follow-up to therapy, is important to the management of patients with coronary artery disease. Whilst limited multi-slice 2D CMR perfusion studies are gaining increased clinical usage for quantifying gross ischaemic burden, research is now directed towards complete 3D coverage of the myocardium for accurate localisation of the extent of possible defects. In 3D myocardial perfusion imaging, a complete volumetric data set has to be acquired for each cardiac cycle in order to study the first pass of the contrast bolus. This normally requires a relatively large acquisition window within each cardiac cycle to ensure a comprehensive coverage of the myocardium and reasonably high resolution of the images. With multi-slice imaging, long axis cardiac motion during this large acquisition window can cause the myocardium imaged in different cross- sections to be mis-registered, i.e., some part of the myocardium may be imaged more than twice whereas other parts may be missed out completely. This type of mis-registration is difficult to correct for by using post-processing techniques. The purpose of this thesis is to investigate techniques for tracking through plane motion during 3D myocardial perfusion imaging, and a novel technique for extracting intrinsic relationships between 3D cardiac deformation due to respiration and multiple ID real-time measurable surface intensity traces is developed. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modelling can be use in real-time prospective motion tracking or correction. In CMR, new imaging sequences have significantly reduced the acquisition window whilst maintaining the desired spatial resolution. Further improvements in perfusion imaging will require the application of parallel imaging techniques or making full use of the information content of the ¿-space data. With this thesis, we have proposed RR-UNFOLD and RR-RIGR for significantly reducing the amount of data that is required to reconstruct the perfusion image series. The methods use prospective diaphragmatic navigator echoes to ensure UNFOLD and RIGR are carried out on a series of images that are spatially registered. An adaptive real-time re-binning algorithm is developed for the creation of static image sub-series related to different levels of respiratory motion. Issues concerning temporal smoothing of tracer kinetic signals and residual motion artefact are discussed, and we have provided a critical comparison of the relative merit and potential pitfalls of the two techniques. In addition to the technical and theoretical descriptions of the new methods developed, we have also provided in this thesis a detailed literature review of the current state-of-the-art in myocardial perfusion imaging and some of the key technical challenges involved. Issues concerning the basic background of myocardial ischaemia and its functional significance are discussed. Practical solutions to motion tracking during imaging, predictive motion modelling, tracer kinetic modelling, RR-UNFOLD and RR-RIGR are discussed, all with validation using patient and normal subject data to demonstrate both the strength and potential clinical value of the proposed techniques.Open acces

A comprehensive multi-scale modeling of defective CdSe colloidal nanocrystals through advanced X-ray scattering techniques

The dissertation includes a comprehensive multi-scale modeling of defective CdSe colloidal nanocrystals through advanced X-ray scattering techniques. Chapter 1 introduces the reader to the entire work of the Ph. D. thesis and to its main topic of research, which is focused on structural and microstructural characterization of colloidal quantum-dots. The following Chapter is dedicated to the description of conventional and unconventional characterization methods at the nanoscale, discussing their limits and potentiality in characterizing real nano-systems. Chapter 3 serves as a mathematical description of the DSE, and its implementation in the DebUsSy suite for the characterization of real ensembles of nanosized samples. Therein, the data collection and reduction procedures are also reported, together with a brief section in which the DSE to PDF approaches are compared. The need of introducing strains and defects in the complex atomistic model of CdSe nanocrystals makes it necessary to describe these defects, with a brief state of the art of their characterization methods (Chapter 4). Chapter 5 is completely dedicated to describing the computational model used for the characterization of cQDs and its use as a part of the overall data analysis strategy. The final Chapters focus on the application of the model to real systems in which its potentiality and sensitivity are tested on different materials, disclosing new size-dependent fault driven relaxation and faceting features in CdSe cQDs. An additional section presents an alternative method for the characterization of metallic NPs with larger sizes, but (much) lower stacking fault probabilities

Variable illumination and invariant features for detecting and classifying varnish defects

This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn\u27t provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction

Variable illumination and invariant features for detecting and classifying varnish defects

This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn't provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction