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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.

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.

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.