Parameter Identification And Fault Detection For Reliable Control Of Permanent Magnet Motors

The objective of this dissertation is to develop new fault detection, identification, estimation and control algorithms that will be used to detect winding stator fault, identify the motor parameters and optimally control machine during faulty condition. Quality or proposed algorithms for Fault detection, parameter identification and control under faulty condition will validated through analytical study (Cramer-Rao bound) and simulation. Simulation will be performed for three most applied control schemes: Proportional-Integral-Derivative (PID), Direct Torque Control (DTC) and Field Oriented Control (FOC) for Permanent Magnet Machines. New detection schemes forfault detection, isolation and machine parameter identification are presented and analyzed. Different control schemes as PID, DTC, FOC for Control of PM machines have different control loops and therefore it is probable that fault detection and isolation will have different sensitivity. It is very probable that one of these control schemes (PID, DTC or FOC) are more convenient for fault detection and isolation which this dissertation will analyze through computer simulation and, if laboratory condition exist, also running a physical models

A Complete Generalized Adjustment Criterion

Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some of them are only sufficient, but not necessary. In this paper, we present a criterion that is necessary and sufficient for four different classes of graphical causal models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the existing ones and in this way unifies adjustment set construction for a large set of graph classes.Comment: 10 pages, 6 figures, To appear in Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI2015

Causal Parameters and Policy Analysis in Economcs: A Twentieth Century Retrospective

The major contributions of twentieth century econometrics to knowledge were the definition of causal parameters when agents are constrained by resources and markets and causes are interrelated, the analysis of what is required to recover causal parameters from data (the identification problem), and clarification of the role of causal parameters in policy evaluation and in forecasting the effects of policies never previously experienced. This paper summarizes the development of those ideas by the Cowles Commission, the response to their work by structural econometricians and VAR econometricians, and the response to structural and VAR econometrics by calibrators, advocates of natural and social experiments, and by nonparametric econometricians and statisticians.

Understanding the costs of urban transportation using causal inference methods

With urbanisation on the rise, the need to transport the population within cities in an efficient, safe and sustainable manner has increased tremendously. In serving the growing demand for urban travel, one of the key policy question for decision makers is whether to invest more in road infrastructure or in public transportation. As both of these solutions require substantial spending of public money, understanding their costs continues to be a major area of research. This thesis aims to improve our understanding of the technology underlying costs of operation of public and private modes of urban travel and provide new empirical insights using large-scale datasets and application of causal econometric modelling techniques. The thesis provides empirical and theoretical contributions to three different strands in the transportation literature. Firstly, by assessing the relative costs of a group of twenty-four metro systems across the world over the period 2004 to 2016, this thesis models the cost structure of these metros and quantifies the important external sources of cost-efficiency. The main methodological development is to control for confounding from observed and unobserved characteristics of metro operations by application of dynamic panel data methods. Secondly, the thesis provides a quantification of the travel efficiency arising from increasing the provision of road-based urban travel. A crucial pre-condition of this analysis is a reliable characterisation of the technology describing congestion in a road network. In pursuit of this goal, this study develops novel causal econometric models describing vehicular flow-density relationship, both for a highway section and for an urban network, using large-scale traffic detector data and application of non-parametric instrumental variables estimation. Our model is unique as we control for bias from unobserved confounding, for instance, differences in driving behaviour. As an important intermediate research outcome, this thesis also provides a detailed association of the economic theory underlying the link between the flow-density relationship and the corresponding production function for travel in a highway section and in an urban road network. Finally, the influence of density economies in metros is investigated further using large-scale smart card and train location data from the Mass Transit Railway network in Hong Kong. This thesis delivers novel station-based causal econometric models to understand how passenger congestion delays arise in metro networks at higher passenger densities. The model is aimed at providing metro operators with a tool to predict the likely occurrences of a problem in the network well in advance and materialise appropriate control measures to minimise the impact of delays and improve the overall system reliability. The empirical results from this thesis have important implications for appraisal of transportation investment projects.Open Acces