Instantaneous complex power applied to three-phase machines

The theory presented in this paper was primarily developed to give a physical interpretation for the instantaneous power flow on a three-phase induction machine, without a neutral conductor, on any operational state and may be extended to any three-phase load. It is a vectorial interpretation of the instantaneous reactive power theory presented by Akagi et al. Which, believe the authors, isn't enough developed and its physical meaning not yet completely understood. This vectorial interpretation is based on the instantaneous complex power concept defined by Torrens for single-phase, ac, steady-state circuits, and leads to a better understanding of the power phenomenon, particularly of the distortion power. This concept has been extended by the authors to three-phase systems, through the utilization of the instantaneous space vectors. The results of measurements of instantaneous complex power on a self-excited induction generator's terminals, during an over-load application transient, are presented for illustration. The compensation of reactive power proposed by Akagi is discussed and a new horizon for the theory application is opened.

Expert system to detect incipient fauts in rotating machines using fuzzy logic and virtual instrumentation

This paper refers to the design of an expert system that captures a waveform through the use of an accelerometer, processes the signal and converts it to the frequency domain using a Fast Fourier Transformer to then, using artificial intelligence techniques, specifically Fuzzy Reasoning, it determines if there is any failure present in the underlying mode of the equipment, such as imbalance, misalignment or bearing defects.

Hybrid methods for lot sizing on parallel machines

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Model selection for discriminative restricted boltzmann machines through meta-heuristic techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.

Electromagnetic torque ripple and copper losses reduction in permanent magnet synchronous machines

This paper presents a method for electromagnetic torque ripple and copper losses reduction in (non-sinusoidal or trapezoidal) surface-mount permanent magnet synchronous machines (SM-PMSM). The method is based on an extension of classical dq transformation that makes it possible to write a vectorial model for this kind of machine (with a non-sinusoidal back-EMF waveform). This model is obtained by the application of that transformation in the classical machine per-phase model. That transformation can be applied to machines that have any type of back-EMF waveform, and not only trapezoidal or square-wave back-EMF waveforms. Implementation results are shown for an electrical converter, using the proposed vectorial model, feeding a non-sinusoidal synchronous machine (brushless DC motor). They show that the use of this vectorial mode is a way to achieve improvements in the performance of this kind of machine, considering the electromagnetic torque ripple and copper losses, if compared to a drive system that employs a classical six-step mode as a converter. Copyright (C) 2011 John Wiley & Sons, Ltd.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

Le aziende vitivinicole: da “machines à produire” a elementi di attrazione nel paesaggio. Proposte e criteri progettuali per l’efficienza funzionale e la qualità architettonica ed ambientale delle strutture ed infrastrutture della trasformazione enologica.

La ricerca ha per oggetto la messa a punto e applicazione di un approccio metaprogettuale finalizzato alla definizione di criteri di qualità architettonica e paesaggistica nella progettazione di aziende vitivinicole medio-piccole, che effettuano la trasformazione della materia prima, prevalentemente di propria produzione. L’analisi della filiera vitivinicola, della letteratura scientifica, della normativa di settore, di esempi di “architetture del vino eccellenti” hanno esplicitato come prevalentemente vengano indagate cantine industriali ed aspetti connessi con l'innovazione tecnologica delle attrezzature. Soluzioni costruttive e tecnologiche finalizzate alla qualità architettonica ed ambientale, attuali dinamiche riguardanti il turismo enogastronomico, nuove funzionalità aziendali, problematiche legate alla sostenibilità dell’intervento risultano ancora poco esplorate, specialmente con riferimento a piccole e medie aziende vitivinicole. Assunto a riferimento il territorio ed il sistema costruito del Nuovo Circondario Imolese (areale rappresentativo per vocazione ed espressione produttiva del comparto vitivinicolo emiliano-romagnolo) è stato identificato un campione di aziende con produzioni annue non superiori ai 5000 hl. Le analisi svolte sul campione hanno permesso di determinare: modalità di aggregazione funzionale degli spazi costruiti, relazioni esistenti con il paesaggio, aspetti distributivi e materico-costruttivi, dimensioni di massima dei locali funzionali alla produzione. Il caso studio relativo alla riqualificazione di un’azienda rappresentativa del comparto è stato utilizzato per la messa a punto e sperimentazione di criteri di progettazione guidati da valutazioni relative alle prestazioni energetiche, alla qualità architettonica e alla sostenibilità ambientale, economica e paesaggistica. L'analisi costi-benefici (pur non considerando le ricadute positive in termini di benessere degli occupanti ed il guadagno della collettività in termini di danni collegati all’inquinamento che vengono evitati in architetture progettate per garantire qualità ambientale interna ed efficienza energetica) ha esplicitato il ritorno in pochi anni dell’investimento proposto, nonostante gli ancora elevati costi di materiali di qualità e dei componenti per il corretto controllo climatico delle costruzioni.

DNA block copolymers – from micelles to machines

DNA block copolymer, a new class of hybrid material composed of a synthetic polymer and an oligodeoxynucleotide segment, owns unique properties which can not be achieved by only one of the two polymers. Among amphiphilic DNA block copolymers, DNA-b-polypropylene oxide (PPO) was chosen as a model system, because PPO is biocompatible and has a Tg < 0 °C. Both properties might be essential for future applications in living systems. During my PhD study, I focused on the properties and the structures of DNA-b-PPO molecules. First, DNA-b-PPO micelles were studied by scanning force microscopy (SFM) and fluorescence correlation spectroscopy (FCS). In order to control the size of micelles without re-synthesis, micelles were incubated with template-independent DNA polymerase TdT and deoxynucleotide triphosphates in reaction buffer solution. By carrying out ex-situ experiments, the growth of micelles was visualized by imaging in liquid with AFM. Complementary measurements with FCS and polyacrylamide gel electrophoresis (PAGE) confirmed the increase in size. Furthermore, the growing process was studied with AFM in-situ at 37 °C. Hereby the growth of individual micelles could be observed. In contrast to ex-situ reactions, the growth of micelles adsorbed on mica surface for in-situ experiments terminated about one hour after the reaction was initiated. Two reasons were identified for the termination: (i) block of catalytic sites by interaction with the substrate and (ii) reduced exchange of molecules between micelles and the liquid environment. In addition, a geometrical model for AFM imaging was developed which allowed deriving the average number of mononucleotides added to DNA-b-PPO molecules in dependence on the enzymatic reaction time (chapter 3). Second, a prototype of a macroscopic DNA machine made of DNA-b-PPO was investigated. As DNA-b-PPO molecules were amphiphilic, they could form a monolayer at the air-water interface. Using a Langmuir film balance, the energy released owing to DNA hybridization was converted into macroscopic movements of the barriers in the Langmuir trough. A specially adapted Langmuir trough was build to exchange the subphase without changing the water level significantly. Upon exchanging the subphase with complementary DNA containing buffer solution, an increase of lateral pressure was observed which could be attributed to hybridization of single stranded DNA-b-PPO. The pressure versus area/molecule isotherms were recorded before and after hybridization. I also carried out a series of control experiments, in order to identify the best conditions of realizing a DNA machine with DNA-b-PPO. To relate the lateral pressure with molecular structures, Langmuir Blodgett (LB) films were transferred to highly ordered pyrolytic graphite (HOPG) and mica substrates at different pressures. These films were then investigated with AFM (chapter 4). At last, this thesis includes studies of DNA and DNA block copolymer assemblies with AFM, which were performed in cooperation with different group of the Sonderforschungsbereich 625 “From Single Molecules to Nanoscopically Structured Materials”. AFM was proven to be an important method to confirm the formation of multiblock copolymers and DNA networks (chapter 5).

Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Non-Linear Analysis and Design of Synchronous Bearingless Multiphase Permanent Magnet Machines and Drives

A two-dimensional model to analyze the distribution of magnetic fields in the airgap of a PM electrical machines is studied. A numerical algorithm for non-linear magnetic analysis of multiphase surface-mounted PM machines with semi-closed slots is developed, based on the equivalent magnetic circuit method. By using a modular structure geometry, whose the basic element can be duplicated, it allows to design whatever typology of windings distribution. In comparison to a FEA, permits a reduction in computing time and to directly changing the values of the parameters in a user interface, without re-designing the model. Output torque and radial forces acting on the moving part of the machine can be calculated. In addition, an analytical model for radial forces calculation in multiphase bearingless Surface-Mounted Permanent Magnet Synchronous Motors (SPMSM) is presented. It allows to predict amplitude and direction of the force, depending on the values of torque current, of levitation current and of rotor position. It is based on the space vectors method, letting the analysis of the machine also during transients. The calculations are conducted by developing the analytical functions in Fourier series, taking all the possible interactions between stator and rotor mmf harmonic components into account and allowing to analyze the effects of electrical and geometrical quantities of the machine, being parametrized. The model is implemented in the design of a control system for bearingless machines, as an accurate electromagnetic model integrated in a three-dimensional mechanical model, where one end of the motor shaft is constrained to simulate the presence of a mechanical bearing, while the other is free, only supported by the radial forces developed in the interactions between magnetic fields, to realize a bearingless system with three degrees of freedom. The complete model represents the design of the experimental system to be realized in the laboratory.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).