Modelling and detection of faults in axial-flux permanent magnet machines

The development of various topologies and configurations of axial-flux permanent magnet machine has spurred its use for electromechanical energy conversion in several applications. As it becomes increasingly deployed, effective condition monitoring built on reliable and accurate fault detection techniques is needed to ensure its engineering integrity. Unlike induction machine which has been rigorously investigated for faults, axial-flux permanent magnet machine has not. Thus in this thesis, axial-flux permanent magnet machine is investigated under faulty conditions. Common faults associated with it namely; static eccentricity and interturn short circuit are modelled, and detection techniques are established. The modelling forms a basis for; developing a platform for precise fault replication on a developed experimental test-rig, predicting and analysing fault signatures using both finite element analysis and experimental analysis. In the detection, the motor current signature analysis, vibration analysis and electrical impedance spectroscopy are applied. Attention is paid to fault-feature extraction and fault discrimination. Using both frequency and time-frequency techniques, features are tracked in the line current under steady-state and transient conditions respectively. Results obtained provide rich information on the pattern of fault harmonics. Parametric spectral estimation is also explored as an alternative to the Fourier transform in the steady-state analysis of faulty conditions. It is found to be as effective as the Fourier transform and more amenable to short signal-measurement duration. Vibration analysis is applied in the detection of eccentricities; its efficacy in fault detection is hinged on proper determination of vibratory frequencies and quantification of corresponding tones. This is achieved using analytical formulations and signal processing techniques. Furthermore, the developed fault model is used to assess the influence of cogging torque minimization techniques and rotor topologies in axial-flux permanent magnet machine on current signal in the presence of static eccentricity. The double-sided topology is found to be tolerant to the presence of static eccentricity unlike the single-sided topology due to the opposing effect of the resulting asymmetrical properties of the airgap. The cogging torque minimization techniques do not impair on the established fault detection technique in the single-sided topology. By applying electrical broadband impedance spectroscopy, interturn faults are diagnosed; a high frequency winding model is developed to analyse the impedance-frequency response obtained

A comparative study of signal processing methods for structural health monitoring

In this paper four non-parametric and five parametric signal processing techniques are reviewed and their performances are compared through application to a sample exponentially damped synthetic signal with closely-spaced frequencies representing the ambient response of structures. The non-parametric methods are Fourier transform, periodogram estimate of power spectral density, wavelet transform, and empirical mode decomposition with Hilbert spectral analysis (Hilbert-Huang transform). The parametric methods are pseudospectrum estimate using the multiple signal categorization (MUSIC), empirical wavelet transform, approximate Prony method, matrix pencil method, and the estimation of signal parameters by rotational invariance technique (ESPRIT) method. The performances of different methods are studied statistically using the Monte Carlo simulation and the results are presented in terms of average errors of multiple sample analyses

Neural Networks for improved signal source enumeration and localization with unsteered antenna arrays

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process

FIRI - a Far-Infrared Interferometer

Half of the energy ever emitted by stars and accreting objects comes to us in the FIR waveband and has yet to be properly explored. We propose a powerful Far-InfraRed Interferometer mission, FIRI, to carry out high-resolution imaging spectroscopy in the FIR. This key observational capability is essential to reveal how gas and dust evolve into stars and planets, how the first luminous objects in the Universe ignited, how galaxies formed, and when super-massive black holes grew. FIRI will disentangle the cosmic histories of star formation and accretion onto black holes and will trace the assembly and evolution of quiescent galaxies like our Milky Way. Perhaps most importantly, FIRI will observe all stages of planetary system formation and recognise Earth-like planets that may harbour life, via its ability to image the dust structures in planetary systems. It will thus address directly questions fundamental to our understanding of how the Universe has developed and evolved - the very questions posed by ESA's Cosmic Vision.Comment: Proposal developed by a large team of astronomers from Europe, USA and Canada and submitted to the European Space Agency as part of "Cosmic Vision 2015-2025

Accurate sound synthesis of 3D object collisions in interactive virtual scenarios

Questa tesi affronta lo studio di algoritmi efficienti per la sintesi di suoni risultanti dalla collisione di oggetti generici, partendo da una descrizione fisica del problema. L'obiettivo della ricerca e' lo sviluppo di strumenti in grado di aumentare l'accuratezza del feedback uditivo in ambienti di realta' virtuale attraverso un approccio basato sulla fisica, senza il bisogno quindi di far riferimento a suoni pre-registrati. Data la loro versatilita' nel trattare geometrie complesse, i metodi agli elementi finiti (FEM) sono stati scelti per la discretizzazione spaziale di generici risonatori tridimensionali. Le risultanti equazioni discrete sono riarrangiate in modo da disaccoppiare i modi normali del sistema tramite l'utilizzo di tecniche di Analisi e Sintesi Modale. Queste tecniche, infatti, portano convenientemente ad algoritmi computazionalmente efficienti per la sintesi del suono. Implementazioni di esempio di tali algoritmi sono state sviluppate facendo uso solo di software open-source: questo materiale a corredo della tesi permette una migliore riproducibilita' dei risultati di questa tesi da parte di ricercatori aventi una preparazione nel campo della sintesi audio. I risultati originali presenti in questo lavoro includono: i tecniche efficienti basate sulla fisica che aiutano l'implementazione in tempo reale di algoritmi di sintesi del suono su hardware comune; ii un metodo per la gestione efficiente dei dati provenienti da analisi FEM che, assieme ad un modello espressivo per la dissipazione, permette di calcolare l'informazione caratterizzante un oggetto risonante e salvarla in una struttura dati compatta iii una trasformazione nel dominio discreto del tempo su due diverse rappresentazioni nello spazio degli stati di filtri digitali del secondo ordine, che permette il calcolo esatto di variabili derivate come la velocita' e l'energia di un risonatore anche quando semplici realizzazioni a soli poli sono impiegate i un'efficiente realizzazione multirate di un banco parallelo di risonatori, derivata usando una suddivisione con Quadrature-Mirror-Filters (QMF). Confrontata con lavori simili presenti in letteratura, questa realizzazione permette l'uso di eccitazione nonlineare in feedback per un banco di risonatori in multirate: l'idea chiave consiste nello svolgere un cambio di stato adattivo nel banco di risonatori, muovendo i risonatori dalla frequenza di campionamento elevata, usata per il processamento della fase transiente, ad un insieme di sottofrequenze ridotte usate durante l'evoluzione in stato libero del sistema.This thesis investigates efficient algorithms for the synthesis of sounds produced by colliding objects, starting from a physical description of the problem. The objective of this investigation is to provide tools capable of increasing the accuracy of the synthetic auditory feedback in virtual environments through a physics-based approach, hence without the need of pre-recorded sounds. Due to their versatility in dealing with complex geometries, Finite Element Methods (FEM) are chosen for the space-domain discretization of generic three-dimensional resonators. The resulting state-space representations are rearranged so as to decouple the normal modes in the corresponding equations, through the use of Modal Analysis/Synthesis techniques. Such techniques, in fact, conveniently lead to computationally efficient sound synthesis algorithms. The whole mathematical treatment develops until deriving such algorithms. Finally, implementation examples are provided which rely only on open-source software: this companion material guarantees the reproducibility of the results, and can be handled without much effort by most researchers having a background in sound processing. The original results presented in this work include: i efficient physics-based techniques that help implement real-time sound synthesis algorithms on common hardware; ii a method for the efficient management of FEM data which, by working together with an expressive damping model, allows to pre-compute the information characterizing a resonating object and then to store it in a compact data structure; iii a time-domain transformation of the state-space representation of second-order digital filters, allowing for the exact computation of dependent variables such as resonator velocity and energy, even when simple all-pole realizations are used; iv an efficient multirate realization of a parallel bank of resonators, which is derived using a Quadrature-Mirror-Filters (QMF) subdivision. Compared to similar works previously proposed in the literature, this realization allows for the nonlinear feedback excitation of a multirate filter bank: the key idea is to perform an adaptive state change in the resonator bank, by switching the sampling rate of the resonators from a common highest value, used while processing the initial transient of the signals at full bandwidth, to a set of lower values in ways to enable a multirate realization of the same bank during the steady state evolution of the signals

Analysis and Synthesis of the Handpan Sound

Handpan is a term used to describe a group of struck metallic musical instruments, which are similar in shape and sound to the Hang, developed by PANArt in January 2000. The Hang is a hand played instrument, which consists of two hemispherical shells of nitrided steel that are fastened together along the circumference. The instrument usually contains a minimum of eight eliptical notes and is played by delivering rapid and gentle strikes to the note areas. Previous studies of the Hang have typically discussed the modes of vibration and sound radiation field when note areas are excited by sinusoidal, hammer, and finger force. It was noted that the manner in which the Hang is played has considerable influence on the spectral content, decay time, and amplitude envelope features produced. This report details the design and implementation of an experimental procedure to record, analyse and synthesise the handpan sound. Four instruments from three different makers were used for the analysis, which gives insight into common handpan sound features, the influence of strike position on spectral content, and the origin of beating phenomena in the signature handpan sound. Subjective listening tests were conducted aiming to estimate the minimum number of vibrational modes required to synthesise the handpan sound

Modeling and fault diagnosis of broken rotor bar faults in induction motors

Due to vast industrial applications, induction motors are often referred to as the “workhorse” of the industry. To detect incipient faults and improve reliability, condition monitoring and fault diagnosis of induction motors are very important. In this thesis, the focus is to model and detect broken rotor bar (BRB) faults in induction motors through the finite element analysis and machine learning approach. The most successfully deployed method for the BRB fault detection is Motor Current Signature Analysis (MSCA) due to its non-invasive, easy to implement, lower cost, reliable and effective nature. However, MSCA has its own limitations. To overcome such limitations, fault diagnosis using machine learning attracts more research interests lately. Feature selection is an important part of machine learning techniques. The main contributions of the thesis include: 1) model a healthy motor and a motor with different number of BRBs using finite element analysis software ANSYS; 2) analyze BRB faults of induction motors using various spectral analysis algorithms (parametric and non-parametric) by processing stator current signals obtained from the finite element analysis; 3) conduct feature selection and classification of BRB faults using support vector machine (SVM) and artificial neural network (ANN); 4) analyze neighbouring and spaced BRB faults using Burg and Welch PSD analysis

State-Space Approaches to Ultra-Wideband Doppler Processing

National security needs dictate the development of new radar systems capable of identifying and tracking exoatmospheric threats to aid our defense. These new radar systems feature reduced noise floors, electronic beam steering, and ultra-wide bandwidths, all of which facilitate threat discrimination. However, in order to identify missile attributes such as RF reflectivity, distance, and velocity, many existing processing algorithms rely upon narrow bandwidth assumptions that break down with increased signal bandwidth. We present a fresh investigation into these algorithms for removing bandwidth limitations and propose novel state-space and direct-data factoring formulations such as * the multidimensional extension to the Eigensystem Realization Algorithm, * employing state-space models in place of interpolation to obtain a form which admits a separation and isolation of solution components, * and side-stepping the joint diagonalization of state transition matrices, which commonly plagues methods like multidimensional ESPRIT. We then benchmark our approaches and relate the outcomes to the Cramer-Rao bound for the case of one and two adjacent reflectors to validate their conceptual design and identify those techniques that compare favorably to or improve upon existing practices

Identification du bruit d'entrée et de sortie sur des moteurs d'avion par antennes microphoniques

Abstract : This thesis considers the discrimination of inlet / exhaust noise of aero-engines in free-field static tests using far-field microphone arrays. Various techniques are compared for this problem, including classical beamforming (CB), regularized inverse method (Tikhonov regularization), LI - generalized inverse beamforming (LI-GIB), clean-PSF, clean-SC and two novel methods which are called hybrid method and clean-hybrid. The classical beamforming method is disadvantaged due to its need for a high number of measurement microphones in accordance with the requirements. Similarly, the inverse method is disadvantaged due to their need of having a priori source information. The classical Tikhonov regularization provides improvements in solution stability, however continues to be disadvantaged due to its requirement of imposing a stronger penalty for undetected source positions. Coherent and incoherent sources are resolved by LI-generalized inverse beamforming (L1-GIB). This algorithm can distinguish the multipole sources as well as the monopoles sources. However, source identification by LI-generalized inverse beamforming takes much time and requires a PC with high memory. The hybrid method is a new regularization method which involves the use of an a priori beamforming measurement to define a data-dependent discrete smoothing norm for the regularization of the inverse problem. Compared to the classical beamforming and the inverse modeling, the hybrid (beamforming regularization) approach provides improved source strength maps without substantial added complexity. Although the hybrid method rather solves the disadvantage of the former methods, the application of this method for identification of weaker sources in the presence of the strong sources isn't satisfactory. This can be explained by the large penalization being applied to the weaker source in the hybrid method, which results in underestimation of source strength for this source. To overcome this defect, the clean-SC method and the proposed clean-hybrid method, which is a combination of the hybrid method and the clean-SC, are applied. These methods remove the effect of the strong sources in source power maps to identify the weaker sources. The proposed methods which represent the main contribution of this thesis show promising results and opens new research avenues. Theoretical study of all approaches is performed for various sources and configurations of array. In order to validate the theoretical study, several laboratory experiments are conducted at Universito de Sherbrooke. The proposed methods have further been applied to the measured noise data from a Pratt & Whitney Canada turbo-fan engine and have been observed to provide better spatial resolution and solution robustness with a limited number of measurement microphones compared to the existing methods.Résumé : La présente thèse étudie la discrimination du bruit d'entrée / de sortie des moteurs d'avion dans des tests statiques en champ libre en utilisant des antennes de microphones en champ lointain. Diverses techniques sont comparées pour ce problème, dont la formation de voie classique (CB), la méthode inverse régularisée (régularisation de Tikhonov), la formation de voies généralisée inverse (L1-GIB), Clean-PSF, Clean-SC et deux méthodes proposées qui s'appellent la méthode hybride et la méthode Clean-hybride. La méthode la formation de voie classique est désavantagée en raison de son besoin de nombreux microphones de mesure. De même, la méthode inverse est désavantagée en raison du besoin d'information a priori sur les sources. La régularisation Tikhonov classique fournit des améliorations dans. la stabilité de la solution; cependant elle reste désavantageuse en raison de son exigence d'imposer une pénalité plus forte pour des positions de source non détectées. Des sources cohérentes et incohérentes peuvent être résolues par la formation de voies généralisée inverse (L1-GIB). Cet algorithme peut identifier les sources multi- polaires aussi bien que les sources monopolaires. Cependant, l'identification de source par la formation de voies généralisée inverse prend beaucoup de temps et exige un ordinateur avec une capacité de mémoire élevée. La méthode hybride est une nouvelle méthode de régularisation qui implique l'utilisation d'un traitement par formation de voie a priori pour définir une norme discrète et dépendante des données pour la régularisation du problème inverse. En comparaison avec la formation de voie classique et la méthode inverse, l'approche hybride (régularisation par formation de voie) fournit des cartographies améliorées d'amplitudes de sources sans aucune complexité supplémentaire substantielle. Bien que la méthode hybride lève les limitations des méthodes classiques, l'application de cette méthode pour l'identification de sources de faible puissance en présence de sources de forte puissance n'est pas satisfaisante. On peut expliquer ceci par la plus grande pénalisation appliquée à la source plus faible dans la méthode hybride, qui aboutit à la sous-estimation de l'amplitude de cette source. Pour surmonter ce défaut, la méthode Clean-SC et la méthode Clean-hybrides proposée qui est une combinaison de la méthode hybride et de Clean-SC sont appliquées. Ces méthodes éliminent l'effet des sources fortes dans les cartographies de puissance de sources pour identifier les sources plus faibles. Les méthodes proposées qui représentent la contribution principale de cette thèse conduisent à des résultats fiables et ouvrent des nouvelles voies de recherche. L'étude théorique de toutes les approches est menée pour divers types de sources et de configurations microphoniques. Pour valider l'étude théorique, plusieurs expériences en laboratoire sont réalisées à Université de Sherbrooke. Les méthodes proposées ont été appliquées aux données de bruit mesurées d'une turbo-soufflante Pratt & Whitney Canada pour fournir une meilleure résolution spatiale des sources acoustique et une solution robuste avec un nombre limité des microphones de mesure comparé aux méthodes existantes