Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Improving the Robustness of Distributed Failure Detectors in Adverse Conditions

Failure detection is at the core of most fault tolerance strategies, but it often depends on reliable communication. We present new algorithms for failure detectors which are appropriate as components of a fault tolerance system that can be deployed in situations of adverse network conditions (such as loosely connected and administered computing grids). It packs redundancy into heartbeat messages, thereby improving on the robustness of the traditional protocols. Results from experimental tests conducted in a simulated environment with adverse network conditions show significant improvement over existing solutions.

Robustness of speckle imaging techniques applied to horizontal imaging scenarios

Atmospheric turbulence near the ground severely limits the quality of imagery acquired over long horizontal paths. In defense, surveillance, and border security applications, there is interest in deploying man-portable, embedded systems incorporating image reconstruction methods to compensate turbulence effects. While many image reconstruction methods have been proposed, their suitability for use in man-portable embedded systems is uncertain. To be effective, these systems must operate over significant variations in turbulence conditions while subject to other variations due to operation by novice users. Systems that meet these requirements and are otherwise designed to be immune to the factors that cause variation in performance are considered robust. In addition robustness in design, the portable nature of these systems implies a preference for systems with a minimum level of computational complexity. Speckle imaging methods have recently been proposed as being well suited for use in man-portable horizontal imagers. In this work, the robustness of speckle imaging methods is established by identifying a subset of design parameters that provide immunity to the expected variations in operating conditions while minimizing the computation time necessary for image recovery. Design parameters are selected by parametric evaluation of system performance as factors external to the system are varied. The precise control necessary for such an evaluation is made possible using image sets of turbulence degraded imagery developed using a novel technique for simulating anisoplanatic image formation over long horizontal paths. System performance is statistically evaluated over multiple reconstruction using the Mean Squared Error (MSE) to evaluate reconstruction quality. In addition to more general design parameters, the relative performance the bispectrum and the Knox-Thompson phase recovery methods is also compared. As an outcome of this work it can be concluded that speckle-imaging techniques are robust to the variation in turbulence conditions and user controlled parameters expected when operating during the day over long horizontal paths. Speckle imaging systems that incorporate 15 or more image frames and 4 estimates of the object phase per reconstruction provide up to 45% reduction in MSE and 68% reduction in the deviation. In addition, Knox-Thompson phase recover method is shown to produce images in half the time required by the bispectrum. The quality of images reconstructed using Knox-Thompson and bispectrum methods are also found to be nearly identical. Finally, it is shown that certain blind image quality metrics can be used in place of the MSE to evaluate quality in field scenarios. Using blind metrics rather depending on user estimates allows for reconstruction quality that differs from the minimum MSE by as little as 1%, significantly reducing the deviation in performance due to user action.

Performance and Robustness Analysis of Co-Prime and Nested Sampling

Coprime and nested sampling are well known deterministic sampling techniques that operate at rates significantly lower than the Nyquist rate, and yet allow perfect reconstruction of the spectra of wide sense stationary signals. However, theoretical guarantees for these samplers assume ideal conditions such as synchronous sampling, and ability to perfectly compute statistical expectations. This thesis studies the performance of coprime and nested samplers in spatial and temporal domains, when these assumptions are violated. In spatial domain, the robustness of these samplers is studied by considering arrays with perturbed sensor locations (with unknown perturbations). Simplified expressions for the Fisher Information matrix for perturbed coprime and nested arrays are derived, which explicitly highlight the role of co-array. It is shown that even in presence of perturbations, it is possible to resolve $O(M^2)$ under appropriate conditions on the size of the grid. The assumption of small perturbations leads to a novel ``bi-affine" model in terms of source powers and perturbations. The redundancies in the co-array are then exploited to eliminate the nuisance perturbation variable, and reduce the bi-affine problem to a linear underdetermined (sparse) problem in source powers. This thesis also studies the robustness of coprime sampling to finite number of samples and sampling jitter, by analyzing their effects on the quality of the estimated autocorrelation sequence. A variety of bounds on the error introduced by such non ideal sampling schemes are computed by considering a statistical model for the perturbation. They indicate that coprime sampling leads to stable estimation of the autocorrelation sequence, in presence of small perturbations. Under appropriate assumptions on the distribution of WSS signals, sharp bounds on the estimation error are established which indicate that the error decays exponentially with the number of samples. The theoretical claims are supported by extensive numerical experiments.

Creating an asset registry for railway electrical traction equipment with open standards

An asset registry arguably forms the core system that needs to be in place before other systems can operate or interoperate. Most systems have rudimentary asset registry functionality that store assets, relationships, or characteristics, and this leads to different asset management systems storing similar sets of data in multiple locations in an organisation. As organisations have been slowly moving their information architecture toward a service-oriented architecture, they have also been consolidating their multiple data stores, to form a “single point of truth”. As part of a strategy to integrate several asset management systems in an Australian railway organisation, a case study for developing a consolidated asset registry was conducted. A decision was made to use the MIMOSA OSA-EAI CRIS data model as well as the OSA-EAI Reference Data in building the platform due to the standard’s relative maturity and completeness. A pilot study of electrical traction equipment was selected, and the data sources feeding into the asset registry were primarily diagrammatic based. This paper presents the pitfalls encountered, approaches taken, and lessons learned during the development of the asset registry.

50 Years of Isolation

The traditional means for isolating applications from each other is via the use of operating system provided “process” abstraction facilities. However, as applications now consist of multiple fine-grained components, the traditional process abstraction model is proving to be insufficient in ensuring this isolation. Statistics indicate that a high percentage of software failure occurs due to propagation of component failures. These observations are further bolstered by the attempts by modern Internet browser application developers, for example, to adopt multi-process architectures in order to increase robustness. Therefore, a fresh look at the available options for isolating program components is necessary and this paper provides an overview of previous and current research on the area.

Robustness in health research : do differences in health measures, techniques, and time frame matter?

Survey-based health research is in a boom phase following an increased amount of health spending in OECD countries and the interest in ageing. A general characteristic of survey-based health research is its diversity. Different studies are based on different health questions in different datasets; they use different statistical techniques; they differ in whether they approach health from an ordinal or cardinal perspective; and they differ in whether they measure short-term or long-term effects. The question in this paper is simple: do these differences matter for the findings? We investigate the effects of life-style choices (drinking, smoking, exercise) and income on six measures of health in the US Health and Retirement Study (HRS) between 1992 and 2002: (1) self-assessed general health status, (2) problems with undertaking daily tasks and chores, (3) mental health indicators, (4) BMI, (5) the presence of serious long-term health conditions, and (6) mortality. We compare ordinal models with cardinal models; we compare models with fixed effects to models without fixed-effects; and we compare short-term effects to long-term effects. We find considerable variation in the impact of different determinants on our chosen health outcome measures; we find that it matters whether ordinality or cardinality is assumed; we find substantial differences between estimates that account for fixed effects versus those that do not; and we find that short-run and long-run effects differ greatly. All this implies that health is an even more complicated notion than hitherto thought, defying generalizations from one measure to the others or one methodology to another.

Power system stability analysis based on descriptive study of electrical indices

In a power network, when a propagation energy wave caused by a disturbance hits a weak link, a reflection is appeared and some of energy is transferred across the link. In this work, an analytical descriptive methodology is proposed to study the dynamical stability of a large scale power system. For this purpose, the measured electrical indices (angle, or voltage/frequency) following a fault in different points among the network are used, and the behaviors of the propagated waves through the lines, nodes and buses are studied. This work addresses a new tool for power system stability analysis based on a descriptive study of electrical measurements. The proposed methodology is also useful to detect the contingency condition and synthesis of an effective emergency control scheme.