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<p>Condition monitoring and fault diagnosis of mechanical systems are two important issues that have received considerable attention from both academia and industry. Several techniques have been developed to date to address these issues. One category of these techniques which has been successfully applied in many industrial plants is based on the multiresolution multivariate analysis algorithms and more specifically the multi-scale principal component analysis (MSPCA). The present research aims to develop a multi-resolution multivariate analysis technique which can be effectively used for fault diagnosis of an internal combustion engine. Crank Angle Domain (CAD) Analysis is the most intuitive strategy for monitoring internal combustion engines. \comment{ as a cyclic system in which events at each cycle is correlated to a particular position of the crankshaft, this leads to analyzing the engine performance in angle domain (i.e. Crank Angle domain for engine) as very logical and intuitive strategy.} Therefore, MSPCA and CAD analysis were combined and a new technique, named CAD-MSPCA, was developed. In addition to this contribution, two indices were defined based on estimation of covariance matrices of scores and fault matrices. These indices were then employed for both fault localization and isolation purposes. In addition to this development, an interesting discovery made through this research was to use the statistical indices , calculated by MSPCA, for fault identification. It is mathematically shown that in case these indices detect a fault in the system, one can determine the spectral characteristics of the fault by performing the spectrum analysis of these indices. This analysis demonstrated the MSPCA as an attractive and reliable alternative for bearing fault diagnosis. These new contributions were validated through simulation examples as well as real measurement data.</p> / Master of Applied Science (MASc)

An automated procedure was developed to identify and extract confined poultry facilities from color 35-mm slide imagery collected by the United States Department of Agriculture/Farm Service Agency (USDA/FSA). The imagery is used by the USDA/FSA to monitor compliance with various farm support programs and to determine crop production acreage within a given county. The imagery is generally available for all counties within the state on an annual basis. The imagery, however, is not flown to rigid specifications as flight height, direction, and overlap can vary significantly. The USDA/FSA attempts to collect imagery with reasonably clear skies, as visual interpretations could be drastically impacted by cloudiness. The goal of this study was to develop procedures to effectively utilize this imagery base to identify and extract poultry facilities using automated techniques based on image processing and GIS. The procedure involved pre-screening the slides to determine coverage, geopositioning to USGS quadrangle base, color scanning to convert slide image to a digital format and archiving each data file with a naming convention that would allow rapid retrieval in later analysis. Image processing techniques were developed for identifying poultry facilities based on spectral characteristics. GIS tools were used to select poultry facilities from an array of features with similar spectral characteristics. A training data set was selected from which the spectral characteristics of poultry facilities were analyzed and compared with background conditions. Poultry facilities were found to have distinguishable characteristics. Descriptive statistics were used to define the range of spectral characteristics encompassing poultry facilities. Thresholding analyses were then utilized to eliminate all image features with spectral characteristics outside of this range. Additional analyses were made to remove noise in the spectral image due to the sun angle, line of sight of camera, variation in roof reflectance due to rust and/or aging, shading by trees, etc. A primary objective in these analyses was to enhance the spectral characteristics for the poultry facility while, at the same time, retaining physical characteristics, i.e. the spectral characteristic is represented by a single blue color with a high brightness value. The techniques developed to achieve a single blue color involved the use of Principal Component Analysis (PCA) on the red color band followed by RGB to Hue and RGB to Saturation analyses on the red and green color bands, respectively, from the resulting image. The features remaining from this series of analyses were converted into polygons (shape file) using ArcView GIS, which was then used to calculate the area and perimeter of each polygon. The parameters utilized to describe the shape of a poultry house included width, length, compactness, length-width ratio, and polygon centroid analysis. Poultry facilities were found to have an average width of approximately 12.6m with a low standard deviation indicating that the widths of all houses were very similar. The length of poultry facilities ranged from 63m to 261m with and average length of 149m. The compactness parameter, which also is related to length and width, ranged from 30 to 130 with a mean value of approximately 57. The shape parameters were used by ArcView GIS to identify polygons that represent poultry facilities. The order of selection was found to be compactness followed by length-width ratio and polygon centroid analysis. A data set that included thirty 35-mm slide images randomly selected from the Rockingham County data set, which contained over 2000 slides, was used to evaluate the automated procedure. The slides contained 182 poultry houses previously identified through manual procedures. Seven facilities were missed and 175 were correctly identified. Ninety-seven percent (97%) of existing poultry facilities were correctly identified which compares favorably with the 97 % accuracy resulted by manual procedures. . The manual procedure described by Mostaghimi, et. al.(1999) only gave the center coordinates for each poultry facility. The automated procedure not only gives the center coordinate for each poultry building but also gives estimates for geometric parameters area, length and width along with an estimate of the capacity of building (i.e. number of birds), and waste load generated by birds including nutrient and bacteria content. The nutrient and bacteria load generated by each poultry facility is important information for conducting TMDL studies currently being developed for impaired Virginia streams. The information is expected to be very helpful to consultants and state agencies conducting the studies. Agricultural support agencies such as USDA/NRCS and USDA/FSA, Extension Service, consultants, etc. will find the information very helpful in the development of implementation plans designed to meet TMDL target water quality goals. The data also should be useful to Water Authorities for selection of appropriate treatment of water supplies and to county and local government jurisdictions for developing policies to minimize the degradation of water supplies. / Ph. D.

I ett alltmer urbaniserat samhälle har forskning uppmärksammat behovet av tillgång till gröna och tysta områden för att främja god hälsa hos människor. Befolkningstillväxten och förtätningen ökar i kommunerna i Stockholms län, vilket orsaker mer trafik och mer buller. Denna studie syftar till att undersöka hur buller från motorvägar sprids i stadsnära naturreservat. Studiens frågeställningar berör vilken inverkan vegetation och topografi har för bullerspridningen, vilka avstånd som krävs från bullerkällan för att bullret inte ska upplevas som störande och slutligen om en mobiltelefon som GNSS-mottagare är tillräcklig för positionering av mätpunkter. Det utvalda studieområdet var Järvafältet, en samling sammanhängande naturreservat omgivet av tätbebyggda kommuner och högtrafikerade motorvägar. Datainsamling bestod av 72 mätpunkter där flertalet data samlades in, bland annat decibelnivå, koordinater, avstånd till närmaste motorväg, höjddata, vindhastighet, väder, vegetation och en subjektiv uppskattning av störning från trafikbuller. Sju av variablerna behandlades i en principal-komponentanalys där den viktigaste komponenten bestod av decibelnivå, avstånd till motorväg, grad av bullerskydd och lufttemperatur. Subjektiv uppskattning av störning ingick inte i principalkomponentanalysen. En ljudmodell skapades med hjälp av interpolationsverktyget Kriging. I principalkomponentanalysen fastslogs att fyra av variablerna byggde upp en extraherad komponent: decibelnivå, avstånd till motorväg, grad av bullerskydd och lufttemperatur. Variablerna decibelnivå och avstånd till motorväg var negativt korrelerade till -0,78. Drygt 500 meter från bullerkällan visade sig sänkningen av decibelnivån plana ut. Även variabeln bullerskydd var korrelerad med decibelnivån, men det gick inte att fastställa om detta samband var mer kopplat till decibelnivån eller till avstånd från motorvägarna. Variabeln vegetation visade liten eller ingen korrelation med decibelnivåer. Lufttemperatur visade viss korrelation med decibelnivå, men detta samband var sannolikt falskt och berodde högst troligen på årstidsväxlingar. Avstånd till motorväg klassificerades i sju kategorier beroende på avstånd till motorväg. I varje kategori testade olika korrelationer mellan decibelnivå och tre olika variabler. Inom cirka 240 meter från motorvägen visade sig graden av bullerskydd vara stark negativt korrelerad med decibelnivå, men längre bort blev denna korrelation svag. Kontrolljudmätningar utfördes efter interpolationsmodellering för att validera resultatet. Kontrollmätningar avvek med i genomsnitt 1,5 dBA. Mobiltelefonens positionerings-precision jämfördes med en dedikerad GNSS-mottagare. Mobiltelefonen avvek med i genomsnitt 8,33 meter (13,16 meter inklusive en utliggare), ett resultat som ansågs fullgott för denna typ av ljudstudie. / In a more urbanised society research has shown the need for green spaces in order to favour human health. This study aims to explore how traffic noise travel through nature reserves close to metropolitan areas. Furthermore, the study tries to answer whether topography and vegetation have impact on noise level, distance required for the noise to stop being an inconvenience and if a mobile phone is sufficient as a GNSS-receiver for positioning of sound measuring points. The study area consists of Järvafältet, north of Stockholm, a collection of nature reserves surrounded by freeways. A total of 72 measuring points were selected, where several data were collected. A principal component analysis was used to extract components from the most important variables: decibel levels, distance to freeways, noise protection and air temperature. Decibel levels and distance to freeways were negatively correlated by -0.78. Vegetations show little correlation with decibel levels and correlation between noise protection and decibel levels are inconclusive but seem to have some correlation when analysed close to the source of noise. Kriging interpolation was used to model sound maps and control measurements resulted in an average deviation of 1,5 dBA in comparison to the created model. It was also found that a mobile phone GNSS receiver is not as precise as a dedicated GNSS receiver but is sufficient for this kind of sound study.

La corrosión de las armaduras es una de las principales causas que afectan a la durabilidad de las estructuras y a su vida útil. La carbonatación del hormigón y la acción de iones agresivos, principalmente los cloruros, son los procesos que mayor riesgo suponen para la corrosión de las armaduras. El control y monitorización mediante técnicas no destructivas es fundamental, de esta forma, se puede obtener información a tiempo real de aquellos factores que pueden favorecer los procesos de corrosión.   En el presente trabajo se exponen los resultados de varios estudios. En primer lugar se detalla el proceso de fabricación, caracterización y evaluación de diferentes tipos de sensores electroquímicos para el control del acceso de iones cloruro y la medida del pH del hormigón. Los sensores se han fabricado en tecnología de microelectrónica híbrida, concretamente en tecnología thick film. A continuación se propone un nuevo modelo de medida de la resistividad en hormigones que permita  valorar de forma indirecta la probabilidad de corrosión de las armaduras. A partir de los resultados obtenidos en un trabajo previo donde se había estudiado la conductividad en una celda electrolítica se desarrolla una metodología alternativa al método directo y al de cuatro puntas que permite monitorizar la evolución de la resistividad de los hormigones endurecidos. Finalmente, se aplican metodologías de análisis multivariante (Principal Component Analysis) en combinación con técnicas electroquímicas dinámicas tradicionales para obtener información del agente que causa la corrosión, por lo que puede ser una herramienta muy útil para el conocimiento fundamental del material metálico objeto de estudio. / Gandía Romero, JM. (2014). Sensores electroquímicos aplicados al estudio de la corrosión en estructuras de hormigón armado [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48516 / Premios Extraordinarios de tesis doctorales

The primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student's t, the Laplace and the Pearson type II distributions as special cases. The result of these extensions is called the Generalized principal component analysis (GPCA). The GPCA is illustrated using both Monte Carlo simulations as well as an empirical study, in an attempt to demonstrate the enhanced reliability of these more general factor models in the context of out-of-sample forecasting. The empirical study examines the predictive capacity of the GPCA method in the context of Exchange Rate Forecasting, showing how the GPCA method dominates forecasts based on existing standard methods, including the random walk models, with or without including macroeconomic fundamentals. / Ph. D. / Factor models are employed to capture the hidden factors behind the movement among a set of variables. It uses the variation and co-variation between these variables to construct a fewer latent variables that can explain the variation in the data in hand. The principal component analysis (PCA) is the most popular among these factor models. I have developed new Factor models that are employed to reduce the dimensionality of a large set of data by extracting a small number of independent/latent factors which represent a large proportion of the variability in the particular data set. These factor models, called the generalized principal component analysis (GPCA), are extensions of the classical principal component analysis (PCA), which can account for both contemporaneous and temporal dependence based on non-Gaussian multivariate distributions. Using Monte Carlo simulations along with an empirical study, I demonstrate the enhanced reliability of my methodology in the context of out-of-sample forecasting. In the empirical study, I examine the predictability power of the GPCA method in the context of “Exchange Rate Forecasting”. I find that the GPCA method dominates forecasts based on existing standard methods as well as random walk models, with or without including macroeconomic fundamentals.

No / Recently, automatic age progression has gained popularity due to its nu-merous applications. Among these is the search for missing people, in the UK alone up to 300,000 people are reported missing every year. Although many algorithms have been proposed, most of the methods are affected by image noise, illumination variations, and most importantly facial expres-sions. To this end we propose to build an age progression framework that utilizes image de-noising and expression normalizing capabilities of kernel principal component analysis (Kernel PCA). Here, Kernel PCA a nonlinear form of PCA that explores higher order correlations between input varia-bles, is used to build a model that captures the shape and texture variations of the human face. The extracted facial features are then used to perform age progression via a regression procedure. To evaluate the performance of the framework, rigorous tests are conducted on the FGNET ageing data-base. Furthermore, the proposed algorithm is used to progress images of Mary Boyle; a six-year-old that went missing over 39 years ago, she is considered Ireland’s youngest missing person. The algorithm presented in this paper could potentially aid, among other applications, the search for missing people worldwide.