As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induced impulses are further modulated by various low frequency signal contents. The compounded effects of interferences, background noise and the combined modulation effects make it difficult to detect bearing faults. This is further complicated by the nonstationary nature of vibration signals due to speed variations in some cases, such as the bearings in a wind turbine. As such, the main challenges in the vibration-based bearing monitoring are how to address the modulation, noise, interference, and nonstationarity matters. Over the past few decades, considerable research activities have been carried out to deal with the first three issues. Recently, the nonstationarity matter has also attracted strong interests from both industry and academic community. Nevertheless, the existing techniques still have problems (deficiencies) as listed below: (1) The existing enveloping methods for bearing fault feature extraction are often adversely affected by multiple interferences. To eliminate the effect of interferences, the prefiltering is required, which is often parameter-dependent and knowledge-demanding. The selection of proper filter parameters is challenging and even more so in a time-varying environment. (2) Even though filters are properly designed, they are of little use in handling in-band noise and interferences which are also barriers for bearing fault detection, particularly for incipient bearing faults with weak signatures. (3) Conventional approaches for bearing fault detection under constant speed are no longer applicable to the variable speed case because such speed fluctuations may cause “smearing” of the discrete frequencies in the frequency representation. Most current methods for rotating machinery condition monitoring under time-varying speed require signal resampling based on the shaft rotating frequency. For the bearing case, the shaft rotating frequency is, however, often unavailable as it is coupled with the instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be determined without knowing the fault type. Additionally, the effectiveness of resampling-based methods is largely dependent on the accuracy of resampling procedure which, even if reliable, can complicate the entire fault detection process substantially. (4) Time-frequency analysis (TFA) has proved to be a powerful tool in analyzing nonstationary signal and moreover does not require resampling for bearing fault identification. However, the diffusion of time-frequency representation (TFR) along time and frequency axes caused by lack of energy concentration would handicap the application of the TFA. In fact, the reported TFA applications in bearing fault diagnosis are still very limited. To address the first two aforementioned problems, i.e., (1) and (2), for constant speed cases, two morphology-based methods are proposed to extract bearing fault feature without prefiltering. Another two methods are developed to specifically handle the remaining problems for the bearing fault detection under time-varying speed conditions. These methods are itemized as follows: (1) An efficient enveloping method based on signal Fractal Dimension (FD) for bearing fault feature extraction without prefiltering, (2) A signal decomposition technique based on oscillatory behaviors for noise reduction and interferences removal (including in-band ones), (3) A prefiltering-free and resampling-free approach for bearing fault diagnosis under variable speed condition via the joint application of FD-based envelope demodulation and generalized demodulation (GD), and (4) A combined dual-demodulation transform (DDT) and synchrosqueezing approach for TFR energy concentration level enhancement and bearing fault identification. With respect to constant speed cases, the FD-based enveloping method, where a short time Fractal dimension (STFD) transform is proposed, can suppress interferences and highlight the fault-induced impulsive signature by transforming the vibration signal into a STFD representation. Its effectiveness, however, deteriorates with the increased complexity of the interference frequencies, particularly for multiple interferences with high frequencies. As such, the second method, which isolates fault-induced transients from interferences and noise via oscillatory behavior analysis, is then developed to complement the FD-based enveloping approach. Both methods are independent of frequency information and free from prefiltering, hence eliminating the tedious process for filter parameter specification. The in-band vibration interferences can also be suppressed mainly by the second approach. For the nonstationary cases, a prefiltering-free and resampling-free strategy is developed via the joint application of STFD and GD, from which a resampling-free order spectrum can be derived. This order spectrum can effectively reveal not only the existence of a fault but also its location. However, the success of this method relies largely on an effective enveloping technique. To address this matter and at the same time to exploit the advantages of TFA in nonstationary signal analysis, a TFA technique, involving dual demodulations and an iterative process, is developed and innovatively applied to bearing fault identification. The proposed methods have been validated using both simulation and experimental data collected in our lab. The test results have shown that the first two methods can effectively extract fault signatures, remove the interferences (including in-band ones) without prefiltering, and detect fault types from vibration signals for constant speed cases. The last two have shown to be effective in detecting faults and discern fault types from vibration data collected under variable speed conditions without resampling and prefiltering.

Rising global energy demands have led to an increase in demand for clean, sustainable energy. A leading technology for reducing greenhouse gas (GHG) emission for existing coal-power infrastructure is gasification, which has sparked an interest in reactor modelling for design and performance analysis. Reduced order models (ROMs) have seen an increase in popularity for entrained flow gasifiers, as they offer a low-computational alternative to conventional computational fluid dynamic (CFD) modelling while maintaining the integrity of important operational parameters, such as carbon conversion and syngas yield. However, ROMs require more physical parameter inputs than are normally required for CFD modelling, such as the geometry of the gas-solid jet (specifically the jet half-angle). Experiments were conducted to understand the relation between the required input parameters for ROMs, such as fuel flow rate, transport gas flow rate, and jet half-angle, and develop useful correlations for ROM systems. A new configuration for pneumatic conveying was developed and tested at the pilot-scale system at NRCan CanmetENERGY. It was used to study the pneumatic conveying of pulverized fuels, specifically the influence of operating parameters such as pressure drop and gas flow rates on the fuel flow rate, and the geometry of the gas-solid fuel jet (notably the jet half-angle) injected into the gasifier. The mean fuel flow rate of pulverized fuels was shown to increase with increasing pressure drop and with decreasing gas flow rates in the fuel transfer line. The jet half-angle was shown to increase as the solid loading ratio in the jet core was decreased. Finally, the relative fuel flow variability was observed to be significantly influenced by the design of the pneumatic conveying system, with the fluctuations increasing with increasing pressure drop and with decreasing gas flow rate, similar to the mean flow rate.

Connaître la probabilité d'occurrence des aléas océano-météorologiques extrêmes est fondamental pour prévenir les risques de submersion marine en zone côtière ou concevoir des aménagements côtiers, portuaires ou des plate-formes offshore. Notamment, le concept de niveau de retour est fréquemment utilisé en ingénierie côtière pour dimensionner des ouvrages de protection. Ces niveaux, dont les périodes de retour d'intérêt se situent généralement entre 100 et 1000 ans, sont habituellement estimés par une analyse statistique locale, à partir de données observées en un site unique. Cependant, la période d'observation est généralement limitée, de sorte que les incertitudes associées aux niveaux de retour élevés sont importantes. L'analyse régionale représente une solution possible pour réduire les incertitudes inhérentes aux analyses locales. Le principe est d'exploiter l'information de sites d'observation provenant d'une région homogène, où les extrêmes sont supposés avoir un comportement probabiliste similaire. L'analyse régionale peut ainsi estimer les niveaux de retour de manière plus fiable qu'une analyse locale. Cependant, son application dans le domaine maritime étant relativement limitée et récente, différentes questions méthodologiques de meurent non-Résolues, comme la formation des régions homogènes ou le traitement de la dépendance entre sites. L'objectif scientifique de la thèse est donc d'approfondir certains points méthodologiques de l'analyse régionale, dans le cadre des aléas maritimes extrêmes. Les points suivants sont abordés en particulier :• Échantillonnage des extrêmes pour l'analyse régionale, à partir des tempêtes détectées via une procédure de declustering spatio-Temporel.• Formation de régions homogènes à partir d'une méthode basée sur l'identification des empreintes typiques des tempêtes.• Prise en compte de la dépendance entre sites d'observation, à travers la construction d'un modèle permettant par exemple d'évaluer la durée effective régionale d'observation ou la période de retour régionale d'une tempête.• Spécification et estimation de la loi régionale, avec incorporation des co-variables influentes, comme la saison d'occurrence ou la direction de provenance pour les vagues.• Comparaison entre analyses locale et régionale, notamment à travers les incertitudes sur les estimations des extrêmes et la capacité à modéliser les horsains présumés.Ces aspects sont illustrés sur des données de hauteurs significatives de vagues et de surcotes de pleine mer, dans la zone Atlantique Nord-Est, Manche et Mer du Nord.Parallèlement, l'objectif applicatif de ces travaux est de contribuer à garantir la sûreté des ouvrages EDF contre le risque de submersion marine. Ceci peut être réalisé grâce à l'exploration de nouvelles techniques d'estimation des aléas maritimes extrêmes telles que l'analyse régionale, qui permet notamment une meilleure prise en compte des horsains. / The knowledge of the probability of occurrence of oceano-Meteorological extremes is essential to prevent risks of coastal flooding or to build coastal protections or off-Shore structures. In particular, the concept of return level is frequently used in coastal engineering to design protection structures. These levels, whose return periods of interest generally lie between 100 and 1000 years, are usually estimated by a local statistical analysis, from data observed at a unique site. However, the period of observation is generally limited, which can imply high uncertainties for high return levels. Regional frequency analysis is a possible solution to reduce uncertainties inherent to local analyses. The principle is to exploit the information of sites of observation from a homogeneous region, where extremes are supposed to share a similar probabilistic behavior. Thus, regional frequency analysis can estimate return levels more accurately than a local analysis. However, its application to the marine field being relatively limited and recent, several methodological questions are still unsolved, such as the formation of homogeneous regions or the dependence between sites. The scientific objective of this thesis is thus to develop some methodological points of regional frequency analysis, in the framework of extreme marine hazards. The following questions are tackled:• Sampling of extremes for regional analysis, from the storms detected through a spatiotemporal declustering procedure.• Formation of homogeneous regions from a method based on the identification of the typical storms footprints.• Consideration of the dependence between sites of observation, through the building of a model allowing, for example, to assess the regional effective duration or the regional return period of a storm.• Specification and estimation of the regional distribution, with the incorporation of influent covariables, such as the season of occurrence or the direction for waves.• Comparison between regional and local analyses, especially through the uncertainties on the estimated extremes and the ability to model the potential outliers. These aspects are illustrated on significant wave height data and skew surge data located in the Northeast Atlantic, the Eastern Channel and the North Sea. At the same time, the industrial objective of this work is to contribute to guarantee the safety of EDF structures against the risk of coastal flooding. This can be achieved through the exploration of new techniques of estimation of extreme marine hazards such as regional frequency analysis, which allows in particular a better representation of outliers

Este trabalho detalha a motivação para a avaliação de desempenho não estacionária de sistemas computacionais e introduz uma abordagem para a modelagem dinâmica de desempenho baseada na análise de resposta em frequência. A modelagem dinâmica é uma abordagem essencial que se desenvolveu como recurso importante na engenharia e algumas ciências naturais por muitas décadas e conta com uma coleção de ferramentas matemáticas para descrever o comportamento dinâmico de sistemas. Seja por tradição ou pela dificuldade de aplicação o uso de modelos dinâmicos em avaliação de desempenho de sistemas computacionais é recente e consideravelmente menos explorada que em outros domínios. A contribuição proposta por esta pesquisa, é a formulação de um arcabouço para avaliação de desempenho não estacionário de sistemas computacionais. O propósito desse arcabouço é produzir um modelo analítico dinâmico experimentalmente construído, que represente a dinâmica que governa o desempenho do sistema. A abordagem é composta por: modelo conceitual para formulação de métricas de desempenho transientes; um método empírico para a obtenção do modelo dinâmico; uma metodologia de análise baseada em resposta de frequência. Usos práticos são ilustrados através de estudos de caso em que os resultados demonstram a aplicabilidade da abordagem. / This research work details the motivation for non-stationary performance evaluation in computer systems and introduces an approach for dynamic performance modeling based on frequencyresponse analysis. Dynamic modeling is an essential approach which developed as an important resource in engineering and natural sciences for many decades and counts on a collection of mathematical tools for describing the dynamic behavior of systems. Whether for tradition or difficulty in applying its methods, the use of dynamic modeling in computer systems performance evaluation is recent and less exploited than in other domains. The contribution proposed by the present research is the formulation of a non-stationary performance evaluation framework. The purpose of this framework is to produce an empiric analytical model which represents the dynamics governing the system performance. The approach comprises: a conceptual model for the formulation of dynamic performance metrics; a method to obtain the dynamic model experimentally; a practical methodology based on frequency-domain analysis. Piratical uses are illustrated by study cases in which results show the applicability of the approach.

The task of detecting and separating multiple radio-frequency signals in a wideband scenario has attracted much interest recently, especially from the cognitive radio community. Many successful approaches in this field have been based on machine learning and computer vision methods using the wideband signal spectrogram as an input feature. YOLO and R-CNN are deep learning-based object detection algorithms that have been used to obtain state-of-the-art results on several computer vision benchmark tests. In this work, YOLOv2 and Faster R-CNN are implemented, trained and tested, to solve the signal separation task. Previous signal separation research does not consider representations other than the spectrogram. Here, specific focus is placed on investigating different time-frequency representations based on the short-time Fourier transform. Results are presented in terms of traditional object detection metrics, with Faster R-CNN and YOLOv2 achieving mean average precision scores of up to 89.3% and 88.8% respectively. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2017. / Saab Grintek Defence / University of Pretoria / Electrical, Electronic and Computer Engineering / MEng (Computer Engineering) / Unrestricted

We often use a vector control for controlling of synchronous motor with permanent magnets. To get this control more effective we need to know the parameters of controlled motor. This document describes several methods, which deal with this problem and they are trying to expand them. We are talking about method uses Ohm’s law and the knowledge of motor model and method uses improved frequency analysis. The text below includes the principles of these methods and also identification verification of parameters by means of simulations on a created motor model. The final result of this work is the library of functions, which identificate on the basis on the improved frequency analysis.

Wetlands are important elements of watersheds that influence water storage, surface water runoff, groundwater recharge/discharge processes, and evapotranspiration. To understand the cumulative effect wetlands have on a watershed, one must have a good understanding of the water-level fluctuations and the storage characteristics associated with multiple wetlands across a region. An improved analytical method is presented to describe the storage characteristics of wetlands in the absence of detailed hydrologic and bathymetric data. Also, a probabilistic approach based on frequency analysis is developed to provide insight into surface and groundwater interactions associated with isolated wetlands. The results of the work include: 1) a power-function model based on a single fitting parameter and two physically based parameters was developed and used to represent the storage of singular or multiple wetlands and lakes with acceptable error, 2) a novel hydrologic characterization applied to 56 wetlands in west-central Florida provided new information about wetland hydroperiods which indicated standing water was present in the wetlands 62% of the time and these wetlands were groundwater recharge zones 59% of the time over the seven year study, 3) the smallest extreme value probability distribution function was identified as the best-fit model to represent the water levels of five wetland categories in west-central Florida, 4) representative probability models were developed and used to predict the water levels of specific wetland categories, averaging less than 10% error between the predicted and recorded water levels, and 5) last, based on this probability analysis, the various wetland categories were shown to exhibit similar means, extremes and ranges in water-level behavior but unique slopes in frequency distributions, a here to for new finding. These results suggest that wetland types may best be differentiated by the regular variability in water levels, not by the mean and/or extreme water levels. The methods and analytical techniques presented in this dissertation can be used to help understand and quantify wetland hydrology in different climatological or anthropogenic stress conditions. Also, the methods explored in this study can be used to develop more accurate and representative hydrologic simulation models.

In addition to a static design, a dynamic analysis has to be performed for bridges for which the maximum permissible train speed exceeds 200 km/h. This analysis requires a lot of computing time, for this reason Svedholm and Andersson (2016) have developed a simple tool describing the relationship between the first eigenfrequency of the bridge, the span length and the minimum mass to fulfill the regulation specified in EN-1990. However, these diagrams are based on 2D beam models in which the 3D dynamic effects are not considered. An evaluation of the torsional modes has been performed by analyzing parametrized 3D bridge models, in order to obtain design diagrams including these effects. To do so, a frequency domain analysis has been implemented, based on a steadystate step previously performed in a FEM software. This approach provides a fast way to solve the equation of motion due to the Fourier transform properties, and allows applying several load configurations which are convenient for a parametric study. From this analysis it can be concluded that the thickness to fulfill the demands is larger for 3D models than for 2D. On one hand, contribution of torsional modes of vibration is more significant for the shortest span length, and on the other hand shear-lag effects lead to a reduction of the total resisting bending section.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18562号 / 工博第3923号 / 新制||工||1603(附属図書館) / 31462 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 寶 馨, 教授 中北 英一, 教授 田中 茂信 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19689号 / 工博第4144号 / 新制||工||1639(附属図書館) / 32725 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 寶 馨, 教授 立川 康人, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM