Dissertations / Theses on the topic 'Incipient fault (IF)'

This dissertation presents a methodology for an efficient, non-destructive, and online incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they become catastrophic. The system provides vital information to help the operator with the decision-making process regarding the condition assessment of the underground cable. It incorporates advanced digital signal processing and pattern recognition methods to classify recorded data into designated classes. Additionally, the IFDS utilizes novel detection methodologies to detect when the cable is near failure. The classification functionality is achieved through employing an ensemble of rule-based and supervised classifiers. The Support Vector Machines, designed and used as a supervised classifier, was found to perform superior. In addition to the normalized energy features computed from wavelet packet analysis, two new features, namely Horizontal Severity Index, and Vertical Severity Index are defined and used in the classification problem. The detection functionality of the IFDS is achieved through incorporating a temporal severity measure and a detection method. The novel severity measure is based on the temporal analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the Global Severity Index (GSI). This index portrays the progressive degradation path of underground cable as catastrophic failure time approaches. The detection approach utilizes the numerical modeling capabilities of SOM as well as statistical change detection techniques. The natural logarithm of the chronologically ordered minimum modeling errors, computed from exposing feature vectors to a trained SOM, is used as the detection index. Three modified change detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms determine the change point or near failure time of cable from the instantaneous values of the detection index. Performance studies using field recorded data were conducted at three warning levels to assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection capability at each warning level. Specifically, it demonstrates that at least one detection technique successfully provides an early warning that a fault is imminent.

The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation.

La détection et le diagnostic des défauts naissants pour les systèmes d’ingénierie ou industriels multivariés à bruit élevé sont abordés dans ce travail de thèse par l’intermédiare d’une approche statistique non paramétrique ’globale’.Un défaut naissant induit un changement anormal dans les valeurs mesurées de la variable du système. Cependant, un tel changement est faible, et tend à ne pas causer de changements évidents dans les paramètres des distributions des signaux du système. En particulier dans un environnement bruité, les caractéristiques de ces défaults faible peuvent être masquées par le bruit et rend celui-ci difficile à évaluer. Dans une telle situation, l’utilisation de méthodes paramétriques traditionnelles pour la détection échouent. Pour faire face à ces difficultés et effectuer la détection et le diagnostic des défauts, une approche’globale’ qui peut prendre en compte la signature totale des défauts est nécessaire. La détection de défauts naissants peut être obtenue par la mesure des différences entre les distributions avant et après l’apparition du défaut. Certaines méthodes basées sur la distribution (dites ’globales’) ont été proposées, mais les performances de détection de ces approches existantes dans un environnement à haut niveau de bruit devraient être améliorées. Dans ce contexte, la divergence de Jensen-Shannon est considérée comme un indicateur de défaut ’global’ pour effectuer la détection et le diagnostic de défaut naissant dans un environnement à haut niveau de bruit. Ses performances de détection pour de petites variations anormales noyées dans le bruit sont validés en simulation. En outre, le problème de l’estimation des défauts est également étudié dans ce travail. Un modèle théorique d’estimation de la sévérité des défauts à parti dépend de la valeur de la divergence pour des conditions Gaussiennes est établi. La précision du modèle d’estimation est évaluée sur des modèles numériques par le biais de simulations. Ensuite, l’approche statistique ’globale’ est mise en oeuvre pour à deux applications dans le domaine de l’ingénierie. La première concerne la détection de fissures naissantes dans un matériau conducteur. La divergence de Jensen-Shannon combinée à l’analyse en composantes indépendantes et à la décomposition on ondelettes a été appliquée à la détection et à la caractérisation de fissures mineures dans des structures conductrices avec des perturbations bruit sur la base de signaux d’impédance expérimentaux. La deuxième application concerne le diagnostic de défauts naissants dans un processus non linéaire multivarié avec un bruit élevé. Le ’Tennessee Eastman Process’ (TEP) est un processus non linéaire multivarié typique pour lequel nous avons appliqué, la divergence de Jensen-Shannon combinée à l’analyse en composantes principales à noyau (ACPN) est pour étudier la détection de défauts naissants dont les difficultés de sont largement décrites dans la littérature
Incipient fault detection and diagnosis in engineering and multivariate industrial systems with a high-level noise are addressed in this Ph.D. thesis by a ’global’ non-parametric statistical approach. An incipient fault is supposed to induce an abnormal change in the measured value of the system variable. However, such change is weak, and it tends not to cause obvious changes in the signal distribution’s parameters. Especially in high noise level environment, the weak fault feature can be masked by the noise and becomes unpredictable. In such a condition, using traditional parametric-based methods generally fails in the fault detection. To cope with incipient fault detection and diagnosis, a ’global’ approach that can consider the total faults signature is needed. The incipient fault detection can be obtained by measuring the differences between the signal distributions before and after the fault occurrence. Some distribution-based ’global’ methods have been proposed, however, the detection capabilities of these existed approaches in high noise level environment should be improved. In this context, Jensen-Shannon divergence is considered a ’global’ fault indicator to deal with the incipient fault detection and diagnosis in a high noise level environment. Its detection performance for small abnormal variations hidden in noise is validated through simulation. In addition, the fault estimation problem is also considered in this work. A theoretical fault severity estimation model depending on the divergence value for the Gaussian condition is derived. The accuracy of the estimation model is evaluated on numerical models through simulations. Then, the ’global’ statistical approach is applied to two applications in engineering. The first one relates to non- destruction incipient cracks detection. The Jensen-Shannon divergence combined with Noisy Independent Component Analysis and Wavelet analysis was applied for detection and characterization of minor cracks in conductive structures with high-level perturbations based on experimental impedance signals. The second application addresses the incipient fault diagnosis in a multivariate non-linear process with a high-level noise. Tennessee Eastman Process (TEP) is one typical multivariate non-linear process, the Jensen-Shannon divergence in the Kernel Principal Component Analysis (KPCA) is developed for coping with incipient fault detection in this process

Recent research has demonstrated the use of electrical signature analysis (ESA), that is, the use of induction motor currents and voltages, for early detection of motor faults in the form of embedded algorithms. In the event of multiple motors energized by a common voltage bus, the cost of installing and maintaining fault monitoring and detection devices on each motor may be avoided, by using bus level aggregate electrical measurements to assess the health of the entire population of motors. In this research an approach for detecting commonly encountered induction motor mechanical faults from bus level aggregate electrical measurements is investigated. A mechanical fault indicator is computed processing the raw electrical measurements through a series of signal processing algorithms. Inference of an incipient fault is made by the percentage relative change of the fault indicator from the “healthy” baseline, thus defining a Fault Indicator Change (FIC). To investigate the posed research problem, healthy and faulty motors with broken rotor bar faults are simulated using a detailed transient motor model. The FIC based on aggregate electrical measurements is studied through simulations of different motor banks containing the same faulty motor. The degradation in the FIC when using aggregate measurements, as compared to using individual motor measurements, is investigated. For a given motor bank configuration, the variation in FIC with increasing number of faulty motors is also studied. In addition to simulation studies experimental results from a two-motor setup are analyzed. The FIC and degradation in the FIC in the case of load eccentricity fault, and a combination of shaft looseness and bearing damage is studied through staged fault experiments in the laboratory setup. In this research, the viability of using bus level aggregate electrical measurements for detecting incipient faults in motors energized by a common voltage bus is demonstrated. The proposed approach is limited in that as the power rating fraction of faulty motors to healthy motors in a given configuration decreases, it becomes far more difficult to detect the presence of incipient faults at very early stages.

Les travaux de cette thèse portent sur la détection et le diagnostic des défauts naissants dans les systèmes d’ingénierie et industriels, par des approches statistiques non-paramétriques. Un défaut naissant est censé provoquer comme tout défaut un changement anormal dans les mesures des variables du système. Ce changement est imperceptible mais aussi imprévisible dû à l’important rapport signal-sur défaut, et le faible rapport défaut-sur-bruit caractérisant le défaut naissant. La détection et l’identification d’un changement général nécessite une approche globale qui prend en compte la totalité de la signature des défauts. Dans ce cadre, la divergence de Kullback-Leibler est proposée comme indicateur général de défauts, sensible aux petites variations anormales cachées dans les variations du bruit. Une approche d’analyse spectrale globale est également proposée pour le diagnostic de défauts ayant une signature fréquentielle. L’application de l’approche statistique globale est illustrée sur deux études différentes. La première concerne la détection et la caractérisation, par courants de Foucault, des fissures dans les structures conductrices. La deuxième application concerne le diagnostic des défauts de roulements dans les machines électriques tournantes. En outre, ce travail traite le problème d’estimation de l’amplitude des défauts naissants. Une analyse théorique menée dans le cadre d’une modélisation par analyse en composantes principales, conduit à un modèle analytique de la divergence ne dépendant que des paramètres du défaut
This phD dissertation deals with the detection and diagnosis of incipient faults in engineering and industrial systems by non-parametric statistical approaches. An incipient fault is supposed to provoke an abnormal change in the measurements of the system variables. However, this change is imperceptible and also unpredictable due to the large signal-to-fault ratio and the low fault-to-noise ratio characterizing the incipient fault. The detection and identification of a global change require a ’global’ approach that takes into account the total faults signature. In this context, the Kullback-Leibler divergence is considered to be a ’global’ fault indicator, which is recommended sensitive to abnormal small variations hidden in noise. A ’global’ spectral analysis approach is also proposed for the diagnosis of faults with a frequency signature. The ’global’ statistical approach is proved on two application studies. The first one concerns the detection and characterization of minor cracks in conductive structures. The second application concerns the diagnosis of bearing faults in electrical rotating machines. In addition, the fault estimation problem is addressed in this work. A theoretical study is conducted to obtain an analytical model of the KL divergence, from which an estimate of the amplitude of the incipient fault is derived

The development and implementation challenges associated with a proposed load allocation paradigm for fault risk assessment and system health management based on uncertain fault diagnostic and failure prognostic information are investigated. Health management actions are formulated in terms of a value associated with improving system reliability, and a cost associated with inducing deviations from a system's nominal performance. Three simulated case study systems are considered to highlight some of the fundamental challenges of formulating and solving an optimization on the space of available supervisory control actions in the described health management architecture. Repeated simulation studies on the three case-study systems are used to illustrate an empirical approach for tuning the conservatism of health management policies by way of adjusting risk assessment metrics in the proposed health management paradigm. The implementation and testing of a real-world prognostic system is presented to illustrate model development challenges not directly addressed in the analysis of the simulated case study systems. Real-time battery charge depletion prediction for a small unmanned aerial vehicle is considered in the real-world case study. An architecture for offline testing of prognostics and decision making algorithms is explained to facilitate empirical tuning of risk assessment metrics and health management policies, as was demonstrated for the three simulated case study systems.

This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) and the key gas method, have limitations such as the "no decision" problem. Various AI techniques may help solve the problems and present a better solution. Based on the IEC 599 standard and industrial experiences, a knowledge-based inference engine for fault detection was developed. Using historical transformer failure data from an industrial partner, a multi-layer perceptron (MLP) modular neural network was identified as the best choice among several neural network architectures. Subsequently, the concept of a hybrid diagnosis was proposed and implemented, resulting in a combined neural network and expert system tool (the ANNEPS system) for power transformer incipient diagnosis. The abnormal condition screening process, as well as the principle and algorithms of combining the outputs of knowledge based and neural network based diagnosis, were proposed and implemented in the ANNEPS. Methods of fuzzy logic based transformer oil/paper insulation condition assessment, and estimation of oil sampling interval and maintenance recommendations, were also proposed and implemented. Several methods of power transformer incipient fault location were investigated, and a 7Ã 21Ã 5 MLP network was identified as the best choice. Several methods for on-load tap changer (OLTC) coking diagnosis were also investigated, and a MLP based modular network was identified as the best choice. Logistic regression analysis was identified as a good auditor in neural network input pattern selection processes. The above results can help developing better power transformer maintenance strategies, and serve as the basis of on-line DGA transformer monitors.
Ph. D.

Monitoring of abnormal events in a distribution feeder by using a single technique is a challenging task. Many abnormal events can cause unsafe operation, including a high impedance fault (HIF) caused by a downed conductor touch ground surface, an incipient fault (IF) caused by partial breakdown to a cable insulation, and a circuit breaker (CB) malfunction due to capacitor bank de-energization to cause current restrikes. These abnormal events are not detectable by conventional protection schemes. In this dissertation, a new technique to identify distribution feeder events is proposed based on the complex Morlet wavelet (CMW) and on a decision tree (DT) classifier. First, the event is detected using CMW. Subsequently, a DT using event signatures classifies the event as normal operation, continuous and non-continuous arcing events (C.A.E. and N.C.A.E.). Additional information from the supervisory control and data acquisition (SCADA) can be used to precisely identify the event. The proposed method is meticulously tested on the IEEE 13- and IEEE 34-bus systems and has shown to correctly classify those events. Furthermore, the proposed method is capable of detecting very high impedance incipient faults (IFs) and CB restrikes at the substation level with relatively short detection time. The proposed method uses only current measurements at a low sampling rate of 1440 Hz yielding an improvement of existing methods that require much higher sampling rates.

The scope of this thesis was to develop a prototype high-impedance arc detection system that a utility worker could use as an early warning system while working in a manhole environment. As part of this system sensors and algorithms were developed to increase the sensitivity of detecting an arc while ignoring loads that can give false positive signatures for arcing. The latest technology was used to repeat measurements performed in previous research from decades ago that lacked in sampling speed and amplitude resolution. Several types of arcs were produced and analyzed so to establish a library of various waveform and frequency signatures. The system was constructed as a development unit and is currently gathering information in the field. Data being collected will be analyzed so future revisions will give higher confidence levels of arc detection. Other future plans involve designing a more compact and portable unit.

Nos sistemas de distribuição de alta e média tensão tem-se aumentado a utilização de linhas de distribuição de energia subterrâneas ou cabos subterrâneos. A ocorrência de faltas nas linhas afeta negativamente a qualidade da energia e o correto funcionamento da rede. O processo que leva a uma falta nos cabos é gradual e está caracterizado por uma série de subciclos de faltas incipientes associadas a uma tensão de arco. Estas, muitas vezes, passam despercebidas e, eventualmente, resultam em uma falta permanente. Os métodos clássicos de localização de faltas como as metodologias baseadas no cálculo da impedância aparente, as baseadas na inteligência artificial e as baseadas nas ondas viajantes são, habitualmente, aplicadas ao sistema depois de uma falta permanente acontecer e precisam de um ou mais ciclos do sinal para entregar uma resposta razoável. No entanto, as faltas nos cabos são um processo gradual, de curta duração (entre ¼ e ½ ciclo do sinal) e seria desejável localizar a falta incipiente antes de tornar-se permanente. Nesse contexto, esta pesquisa aborda o problema de localização de faltas incipientes. Assim, nesta tese propõe-se uma nova técnica de localização de faltas incipientes usando medições em um terminal, no domínio do tempo e que utiliza componentes de fase. Desta forma, são desenvolvidas duas novas formulações do modelo elétrico do sistema de distribuição com cabos subterrâneos durante uma falta incipiente. A abordagem proposta considera simultaneamente na sua formulação características da falta incipiente e dos sistemas de distribuição de energia, como a tensão de arco, o modelo Π nominal de parâmetros concentrados do cabo subterrâneo, o desequilíbrio do sistema e a condição da carga. A estimativa da distância da falta, junto com os parâmetros da falta incipiente, é obtida a partir da solução de um sistema sobredeterminado de equações lineares pela aplicação do método de mínimos quadrados ponderados não negativos. As formulações propostas permitem estimar a distância da falta em termos da reatância da linha até a falta. Além disso, é proposto um processo de compensação de corrente para estimar a corrente de falta; é aplicado um pré-processamento dos dados de entrada para suavizar o efeito do ruído que pode conter o sinal e, é aplicado um pós-processamento dos resultados para refinar e entregar a melhor estimativa obtida durante o processo de localização da falta. O desempenho da técnica proposta é avaliado mediante estudos de casos simulados em um circuito real de distribuição no Alternative Transients Program (ATP/EMTP) considerando análises de sensibilidade e comparativa. Também, o modelo da falta incipiente foi programado utilizando a ferramenta de MODELS do ATP/EMTP. Os resultados obtidos, considerando faltas incipientes simuladas que avaliam a influência da variação da magnitude de tensão de arco, do ruído aleatório inserido na tensão de arco, da distância da falta, da taxa de amostragem, do carregamento do sistema, do modelo de tensão de arco e de incertezas nas medições, indicam claramente que a abordagem proposta possui validade como técnica de localização de faltas incipientes, apresentando erros médios globais de 1,60% e 0,93%, respectivamente para cada formulação proposta.
The use of underground power distribution lines or underground cables in the high and medium voltage distribution systems has increased dramatically in recent years. The fault occurrence in the distribution lines negatively affects the power quality and the correct network operation. The process which leads to a fault in underground cables is gradual and is characterized by a series of sub-cycles of incipient faults associated with an arc voltage. These often are unnoticed and, eventually, results in a permanent fault. Classical fault localization methods such as the based-impedance, the based on artificial intelligent and the based on traveling waves are, usually, applied to the system after a permanent fault occurrence and need one or more signal cycles for providing a reasonable response. However, the faults in cables are a gradual process, with short duration (between ¼ to ½ of signal cycle) and would be desirable to locate the fault before this becomes a permanent fault. In this context, this research approaches the incipient faults location problem. Thus, in this thesis is proposed a new incipient fault location technique using single-end terminal measurement, in time-domain and employing phase components. In this way, two new formulations of the electrical model of the distribution system with underground cables during an incipient fault are developed. The proposed approach considers simultaneously in its formulation, incipient fault type and power distribution systems characteristics as arc voltage, unbalanced operation, load conditions and complete line model. The fault distance estimation, together with the incipient fault parameters, it is obtained from the solution of an overdetermined linear system of equations by the application of the non-negative weighted least squares estimator method. The proposed formulations allow estimating the fault distance in terms of the line reactance up to the fault. In addition, a load current compensation strategy is proposed to reduce its effect in the fault current estimation; an input data pre-processing is applied to smooth out the noise effect and a post-processing of the results is performed for estimation refinement and to provide the best estimate obtained during the fault location process. The proposed technique performance is evaluated through simulated cases studies in a real-life distribution network with underground cable data in the Alternative Transients Program (ATP/EMTP) considering sensitivity and comparative analyzes. Also, the fault model was programmed using the MODELS tool of ATP/EMTP. The obtained results, considering simulated incipient faults, which evaluate the influence of variations in the arc voltage magnitude, random noise percentage inserted in the arc voltage, fault distance, sampling rate, load dynamics, the arc voltage model and uncertainties in measurements, indicate clearly that the proposed approach is valid as incipient faults location technique, showing overall average errors of 1,60% and 0,93%, respectively for each proposed formulation.

Les véhicules électriques (VEs) connaissent un développement en plein essor ces dernières années pour faire face aux problèmes environnementaux et aux dérèglements climatiques. Du point de vue du stockage de l'énergie, c'est essentiellement la technologie des batteries lithium-ion (LIB) qui est la plus utilisée pour d'alimentation des véhicules électriques compte tenu de leur haute densité d'énergie / puissance et de leur longue durée de vie. La fiabilité des LIBs est sans aucun doute d'une importance fondamentale pour le développement des VE. Dans cet objectif, les travaux de thèse s'inscrivent dans le développement des algorithmes dédiés à l'estimations des états de la batterie ainsi qu'au diagnostic de court-circuit naissant. L'estimation des états de la batterie, qui peut également être qualifiée de surveillance de la batterie, est un élément indispensable de la stratégie de gestion de l'énergie d'un véhicule électrique ou hybride. Par ailleurs, le vieillissement prématuré peut être évité grâce à la surveillance des états de batterie telles que l'état de charge (SOC) et l'état de santé (SOH). De plus, étant donné que l'emballement thermique (TR) peut être la conséquence d'un défaut de court-circuit (SC) électrique, de ce fait, une détection efficace de SC naissant de la batterie peut donc donner une alerte protectrice de TR. La principale contribution de cette thèse réside dans les aspects théoriques et méthodologiques dans le domaine de la surveillance de la batterie et du diagnostic SC naissant
In order to cope with environmental problems and climate change, electric vehicles (EVs) gain the ever booming development in recent years. From the point of view of energy storage, because of their high energy / power density and their extended lifespan, it is essentially the lithium-ion battery (LIB) technology which is the most used power unit for EVs. Doubtlessly, the reliability of LIBs is of vital importance for the development of EVs. To this end, this thesis is dedicated to the algorithmic development of battery state and parameter estimation as well as incipient short-circuit diagnosis. The battery state and parameter estimation, which can also be termed as battery monitoring, is a critical part in the so-called health conscious energy management strategy for electric or hybrid electric vehicle. Premature aging can be avoided through the accurate battery state estimation such as state of charge (SOC) and state of health (SOH). Furthermore, as the thermal runaway (TR) can be ultimately attributed to short-circuit (SC) electrical abuse, therefore, effective battery incipient SC detection can give an early warning of TR. The main contribution of this thesis lies in the theoretical and methodological aspects in the domain of battery monitoring and incipient SC diagnosis

Grâce à leurs avancées techniques en termes de poids, performances et fiabilité, les machines synchrones sont de plus en plus utilisées dans le domaine de transport et en particuliers dans l’aéronautique. Les stratégies de maintenances de ces systèmes électriques sont alors indispensables afin d'éviter des surcouts liés à des temps d'arrêt non planifiés. Ce document propose une analyse des conséquences d'un défaut inter-spires intermittent naissant dans l'enroulement statorique d'une Machine Synchrone à Aimants Permanents (MSAP). Ce type de défaut correspond à l'état peu avancé d'un futur court-circuit permanent. Jusqu'à présent, les études menées se sont limitées à la détection de courts-circuits inter-spires permanents. L'objectif de cette analyse est de définir une méthode de détection de ce type de défaut qui soit facile à mettre en œuvre. A partir d'une étude analytique du courant statorique d'une MSAP contrôlée en courant en présence de court-circuit intermittent, nous avons étudié l'impact des différentes grandeurs influençant la perturbation du courant. Nous avons constaté que la forme de la perturbation créée par le défaut était toujours la même et qu'elle était la signature du défaut intermittent dans le courant. Par la suite cette étude analytique a été validée expérimentalement. Dans la partie suivante nous avons étudié la sensibilité des méthodes de détection des courts-circuits inter-spires permanents appliquées au cas du court-circuit intermittent. Ces méthodes se sont révélées inadaptées pour la détection du défaut étudié dans ce travail. Nous avons donc proposé une méthode dédiée qui est basée sur la détection de la signature du défaut et qui utilise une transformation en ondelette adaptée. Il s'agit d'une méthode de détection de forme qui permet non seulement de détecter le défaut intermittent mais aussi de le distinguer des autres types de défauts. La performance de la méthode a été validée par les résultats de simulation et de manipulation. Dans une dernière partie, une étude plus générale sur le suivi de vieillissement des enroulements est proposée. Elle est basée sur le suivi de l'évolution des courbes d'admittance hautes fréquences d'un bobinage au cours du temps en utilisant les fonctions de transfert de ce dernier
Thanks to technical advances in terms of weight, performance and reliability, synchronous machines are increasingly used in the transport field and especially in aeronautics. The maintenance strategies of these electrical systems are essential to avoid extra costs associated with unscheduled downtime. This document offers a study on the intermittent inter-turn fault occurring in the stator winding of a Permanent Magnet Synchronous Machine (PMSM) and its consequences. This type of fault correspond to the emerging state for a future permanent short circuit condition. So far, studies have been limited to the detection of continuous inter-turn short circuits. The main purpose of this analysis is to define a detection method for this type of fault easy to implement. Based on the stator current analytical study of a PMSM current controlled in presence of intermittent short circuit, we had studied the impact of different variables influencing the current disturbance. We had found that the shape of the disturbance created by the fault was always the same and that it was the fault signature in the current signal. Later this analytical study was validated experimentally. In the next part we had studied the sensitivity of continuous short circuits detection methods applied in the case of intermittent short circuit. These methods have been proved unsuitable to detect the defect studied in this work. Therefore, we had proposed a dedicated method based on the fault signature identification using an adapted wavelet transform. It is a pattern detection method able to detect the intermittent fault and to distinguish it from other types of defects. The performance of the method was validated by simulation and experimental results. In the last part, a more general study concerning the winding health monitoring is proposed. It uses transfer functions and it is based on the monitoring over time of the winding high frequencies admittance curves evolution

Thesis (Ph. D.)--University of Missouri--Rolla, 2007.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed February 4, 2008) Includes bibliographical references.

The rolling element bearing is a key component of many machines. Accurate and timely diagnosis of its faults is critical for proactive predictive maintenance. The research described in this thesis focuses on the development of techniques for detecting and diagnosing incipient bearing faults. These techniques are based on improved dynamic models and enhanced signal processing algorithms. Various common fault detection techniques for rolling element bearings are reviewed in this work and a detailed experimental investigation is described for several selected methods. Envelope analysis is widely used to obtain the bearing defect harmonics from the spectrum of the envelope of a vibration signal. This enables the detection and diagnosis of faults, and has shown good results in identifying incipient faults occurring on the different parts of a bearing. However, a critical step in implementing envelope analysis is to determine the frequency band that contains the signal component corresponding to the bearing fault (the one with highest signal to noise ratio). The choice of filter band is conventionally made via manual inspection of the spectrum to identify the resonant frequency where the largest change has occurred. In this work, a spectral kurtosis (SK) method is enhanced to enable determination of the optimum envelope analysis parameters, including the filter band and centre frequency, through a short time Fourier transform (STFT). The results show that the maximum amplitude of the kurtogram indicates the optimal parameters of band pass filter that allows both outer race and inner race faults to be determined from the optimised envelope spectrum. A performance evaluation is carried out on the kurtogram and the fast kurtogram, based on a simulated impact signal masked by different noise levels. This shows that as the signal to noise ratio (SNR) reaches as low as -5dB the STFT-based kurtogram is more effective at identifying periodic components due to bearing faults, and is less influenced by irregular noise pulses than the wavelet based fast kurtogram. A study of the accuracy of rolling-bearing diagnostic features in detecting bearing wear processes and monitoring fault sizes is presented for a range of radial clearances. Subsequently, a nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics. Simulation results indicate that the vibrations at fault characteristic frequencies exhibit significant variability for increasing clearances. An increased vibration level is detected at the bearing characteristic frequency for an outer race fault, whereas a decreased vibration level is found for an inner race fault. Outcomes of laboratory experiments on several bearing clearance grades, with different local defects, are used herein for model validation purposes. The experimental validation indicates that the envelope spectrum is not accurate enough to quantify the rolling element bearing fault severity adequately. To improve the results, a new method has been developed by combining a conventional bispectrum (CB) and modulation signal bispectrum (MSB) with envelope analysis. This suppresses the inevitable noise in the envelope signal, and hence provides more accurate diagnostic features. Both the simulation and the experimental results show that MSB extracts small changes from a faulty bearing more reliably, enabling more accurate and reliable fault severity diagnosis. Moreover, the vibration amplitudes at the fault characteristic frequencies exhibit significant changes with increasing clearance. However, the vibration amplitude tends to increase with the severity of an outer race fault and decrease with the severity of an inner race fault. It is therefore necessary to take these effects into account when diagnosing the size of a defect.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico
Power transformers play an important role in the power energy supply, demanding the continuous monitoring of process that could lead to operation faults, specifically in the equipment electric insulation. Methods considered in the standards IEC, IEEE and NBR7274 for dissolved gas analysis (DGA) do not provide good levels of accuracy, because there are scenarios with oil samples that lead to contradictory evaluations. The extension theory is a method based on the idea that contradiction can be transformed into compatible problems. In this work, this theory is applied for solving incompatibilities found in the application of the standard NBR7274 for incipient detection faults (the modified Roger s Method). In order to validate the approach, several test cases from recent literature have been implemented and included. The approach has shown good comparative performance in the fault identification process.
Os transformadores de potência cumprem um papel decisivo na continuidade do fornecimento de energia elétrica, obrigando um monitoramento contínuo dos processos que possam provocar falhas de operação, que particularmente ocorrem no isolamento do equipamento. Os métodos previstos na IEC, IEEE e NBR7274 para análise do gás dissolvido (AGD), não alcançam nível pleno de acerto, pois existem situações nas amostras de óleo que geram resultados contraditórios e incompatíveis. A Teoria da Extensão é um método baseado na idéia de que contradições podem ser transformadas em problemas compatíveis. Neste trabalho tal metodologia é aplicada para resolver incompatibilidades encontradas no diagnóstico feito com a NBR7274 (método de Rogers modificado) para detecção de falhas incipientes. O método consiste em modelar a NBR e analisar os resultados quanto ao grau de acerto obtido. Foram feitos testes com vários estudos de caso disponíveis na literatura técnica, mostrando-se bem promissor na identificação das falhas.

O monitoramento de vibração de máquinas rotativas é de grande interesse da indústria, uma vez que se tem a possibilidade de detectar, com certa antecedência, problemas relacionados à condição de funcionamento do equipamento, possibilitando reparos, ajustes ou consertos e, assim, minimizando custos no caso de falhas graves ou paradas inesperadas. Neste trabalho é feita a adequação de uma bancada de testes para estudo de técnicas de detecção de falhas em rolamentos de esferas por análise de vibração. O objetivo é o de localizar falhas em rolamentos utilizando o monitoramento dos sinais das vibrações (aceleração) no mancal que suporta o rolamento de teste e o posterior processamento com o auxílio de alguma técnica de detecção. Neste trabalho a técnica do envelope foi escolhida. A bancada, assim desenvolvida, permite a retirada e colocação dos rolamentos em estudo de maneira simples e rápida, permitindo a execução de diversos testes com rapidez. A investigação da detecção de falhas em rolamentos é feita comparando-se rolamentos em condições normais com rolamentos que têm um defeito induzido. Inicialmente é apresentada a descrição das origens e formas de propagação das falhas em elementos de rolamentos e também as formas como estas falhas são induzidas em locais específicos de cada rolamento. Uma breve revisão sobre métodos de detecção de falhas no domínio do tempo e da frequência é feita. Ênfase é dada ao método do envelope que faz uso da transformada de Hilbert (Hilbert Transform) conjuntamente com a transformada Rápida de Fourier (Fast Fourier Transform). Em seguida, descreve-se como foram geradas as falhas e separados os grupos de teste, também é descrito como os ensaios foram executados com os danos induzidos em local pré-determinado no rolamento. Os resultados obtidos possibilitaram identificar as origens das falhas induzidas tanto na pista externa quanto na interna dos rolamentos analisando os sinais de vibração do mancal onde está montado o rolamento em teste com o pós-processamento dos mesmos com a técnica investigada. Os testes simulam as condições extremas encontradas em sistemas rotativos de equipamentos industriais através da imposição de cargas verticais ao rolamento. Este estudo de detecção das falhas em rolamentos propiciou uma melhor compreensão e análise do processo de falha nestes componentes.
The vibration monitoring of rotating machines is of great interest to industries since it has the ability to detect, in advance, problems related to the operational condition of the equipment, enabling fixing, adjustments or repair, and thus, minimizing the cost in case of faults or unexpected downtime. In this work, the study of the adequacy of a test bench for detecting faults in ball bearings by vibration analysis techniques is developed. It aims at locating faults in ball bearings using vibration monitoring signals (acceleration) in the journal bearings that support this elements and further processing with the aid of some detection technique. In this work the envelope technique was chosen. The developed test bench thus allows the removal and placement of the bearings in a simple and fast way allowing running quickly several tests. The investigation of detecting faults in ball bearings is made by comparing the bearings under normal conditions with bearings which have an induced defect. First of all is presented a description of the origins and forms of propagation of faults in bearing elements and also the ways in which these failures are induced in specific locations of each bearing. A brief review of methods for detecting faults in the time and frequency domain is made. Emphasis is given to the Envelope Method which uses the Hilbert Transform with the Fast Fourier Transform. Then it is described how the tests were accomplished with the induced damage in predetermined sites in the bearing. The results obtained allowed to identify the origins of the induced bearing failures in both outer and inner races just by reading the vibration signals and post-processing them with the investigated technique. The tests take care to simulate conditions close to those found in actual rotatory systems of industrial equipment by imposing vertical loads to the bearing. This study in detecting flaws in balls bearings provided a better understanding of the analysis failure process in these components.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior
O diagnÃstico de falhas incipientes em transformadores de potÃncia imersos em Ãleo està diretamente relacionado à avaliaÃÃo das condiÃÃes do sistema de isolamento. Este estudo aborda a relaÃÃo entre os gases dissolvidos no Ãleo e a qualidade do Ãleo mineral isolante utilizado em transformadores de potÃncia. As redes neurais artificiais sÃo utilizadas na abordagem da avaliaÃÃo das condiÃÃes operacionais do Ãleo isolante em transformadores de potÃncia, que à caracterizada por um comportamento dinÃmico nÃo-linear. As condiÃÃes de operaÃÃo e a integridade do sistema de isolamento de um transformador de potÃncia podem ser inferidas atravÃs das anÃlises fÃsico-quÃmicas e cromatogrÃficas (AnÃlise de GÃs Dissolvido). Estes ensaios permitem estabelecer procedimentos de operaÃÃo e manutenÃÃo do equipamento e normalmente sÃo realizados simultaneamente. Esta tese de doutorado propÃe um mÃtodo que pode ser usado para extrair informaÃÃes cromatogrÃficas usando as anÃlises fÃsico-quÃmicas atravÃs de redes neurais artificiais. As anÃlises atuais das propriedades fÃsico-quÃmicas fornecem apenas diagnÃstico do estado do Ãleo, o que nÃo permite o diagnÃstico de falhas incipientes. Acredita-se que, as concessionÃrias de energia podem melhorar a confiabilidade na previsÃo de falhas incipientes a um custo menor com este mÃtodo, uma vez que apenas um ensaio à necessÃrio. Os resultados mostraram que esta estratÃgia à promissora com mÃdia de acertos em diagnÃsticos de falhas maiores que 72%. O objetivo deste trabalho à a aplicaÃÃo direta do diagnÃstico de falhas incipientes atravÃs da utilizaÃÃo de propriedades fÃsico-quÃmicas, sem a necessidade de fazer uma cromatografia do Ãleo.
The diagnosis of incipient fault in power transformers immerses in oil are directly related to the assessment of the isolation system conditions. This search is about the relationship between dissolved gases and the quality of the insulating mineral oil used in power transformers. Artificial Neural Networks are used to approach operational conditions assessment issue of the insulating oil in power transformers, which is characterized by a nonlinear dynamic behavior. The operation conditions and integrity of a power transformer can be inferred by analysis of physicochemical and chromatographic (DGA â Dissolved Gas Analysis) profiles of the isolating oil. This tests allow establishing procedures for operating and maintaining the equipment and usually are performed simultaneously. This work proposes a method that can be used to extract chromatographic information using physicochemical analysis through Artificial Neural Networks. The present analysis of physicochemical properties only provide a diagnostic tool for the oil quality, which does not allow the diagnosis of incipient faults. ItÂs believed that, the power utilities could improve reliability in the prediction of incipient failures at a lower cost with this method, since only one test is required. The results show this strategy might be promising with an average accuracy for diagnosis of faults greater than 72%. The purpose of this work is the direct implementation of the diagnosis of incipient faults through the use of physicochemical properties without the need to make an oil chromatography.

Automated fault location systems use power quality monitoring and circuit data to provide with a distance or impedance estimate to the fault. This can be used to avoid manual patrolling of the entire feeder in case of a main feeder lockout. It can also be used for circuits with repeated momentary interruptions to pinpoint the section of the circuit causing such problems. Self clearing sub cycle faults have been identified as the precursors of a number of sustained faults (requiring the operation of protective device) in utility distribution networks. The frequency of such incipient faults increases considerably as they are about to evolve into a full blown fault. This report proposes a modified and improved fault location algorithm that can be used to accurately identify sustained as well as temporary faults. The algorithm is based in the time domain and takes into account the arc voltage during a fault event. The proposed algorithm is developed, validated and applied to known distribution field data. Time domain simulation models are also used for validation purposes. The developed algorithm was observed to be very accurate when compared to other impedance based fault location algorithms proposed in the literature. Finally, sub cycle event identification and fault pre-location is proposed that can be very useful for electric utility operations. Highly accurate results were observed during this application study. For instance, a current waveform containing three incipient and one full fault event is shown in the figure given below. The estimated reactance to an incipient fault location is approximately 1.1 Ω. The fault location results obtained from the first three sub-cycle faults can be used to avert the final sustained fault event.
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碩士
國立中正大學
電機工程研究所
104
In recent years, the widespread use of power quality (PQ) monitors and research advancements in a new research field named power quality data analytics. Power quality disturbance data is increasingly applied to extract useful information about the conditions of power system, such as monitoring incipient equipment failures, and solve various power system problems based on the information. In this thesis, abnormalities are detected by comparing with and without disturbances. Kullback-Leibler divergence (KLD) is used to assess the difference of the distributions. An abnormality exists if the KLD is larger than a threshold. The KLD could be used as features. To precisely forecast the incipient fault event, k-nearest neighbors (KNN) and support vector machine (SVM) are used to classify different abnormal waveforms with features of numerous abnormal events. With extend of characteristic of KLD, a diagnosis method is proposed. Results show that the system can provide fast and accurate fault forecast in the power system.

博士
國立高雄應用科技大學
電機工程系碩士班
95
Based on statistical learning theory (SLT), a support vector machine (SVM) has been well recognized as a powerful computational tool for problems with nonlinearity that have high dimensionalities. Solving the problem of feature and kernel parameter selection is a difficult task in machine learning and of high practical relevance in blurred incipient fault diagnosis of power transformers. The feasibility of applying an artificial neural network (ANN) and multi-layer SVM with feature and radial basis function (RBF) kernel parameter selection to diagnose incipient fault of power transformers by combining a clonal selection algorithm (CSA) has been explored for the first time. The CSA encoding technique is applied to improve the accuracy of classification; which removed redundant input features that may be confusing the classifier and optimized the parameters selection at the same time. Content of five diagnostic gases dissolved in oil obtained by dissolved gas analysis (DGA) is preprocessed through a special data processing, and 36 features are extracted as input vectors for classification. Then two classifiers are trained and validated with the training dataset and validating dataset which are extracted by the data preprocessing procedures respectively. Experimental results of practical data demonstrate the effectiveness and improved efficiency of the proposed approach, make operation faster, and also increase the accuracy of the classification.

碩士
國立中正大學
電機工程研究所
103
With the advance of technologies, the need of better quality of electricity for living becomes an important issue. The disturbances occurred in the power system may cause voltage and current deviation from their nominals. It can result in serious damages or equipment malfunctions. Therefore, the forecast of transmission line fault is one of the crucial studies in the power system. If an incipient fault occurs in the power system, rapid forecasting of the fault location can avoid upcoming series faults and reduce outage times, as well as mitigate losses. In this thesis, power quality meters are adopted to record the fault event in the power system. To precisely forecast the incipient fault event, support vector machine (SVM) is combined with discrete wavelet transform (DWT) and discrete Fourier transform (DFT) to extract features from numerous abnormal event of a grid. Results show that the proposed method can provide fast and accurate fault forecast in the power system.

碩士
國立臺灣科技大學
電機工程技術研究所
86
The dissolve gas analysis techniques have been frequently adopted to diagnose the incipient fault of oil-filled power transformers such that the quality of energy supply and equipment security can be guaranteed. Therefore, it is meaningful to promote the accuracy of diagnostic methods.To increase the accuracy of conventionally-used Rogers ratio method(RRM) may not be justified from the viewpoint of principal components analysis which is applied on the results of RRM. Based on this observation, a neural network based diagnostic method is proposed. This method is also compared with two conventionally- used methods, gas pattern analysis(GPA) and discriminant analysis(DA), to investigate its feasibility.Practical data of faulted case from Taiwan Power Company have been utilized to test the proposed method. From the simulation results, its accuracy is above 90% which is slightly better than GPA and DA. Owing to the potential of the neural network technique, the proposed method is justified to conduct further study

碩士
國立中正大學
電機工程研究所
106
Permanent faults of power system components may result in serious damages or equipment malfunction. The incipient fault detection is considered a crucial research topic in power system studies. The incipient faults which are originally self-cleaning faults would repeatedly occur and gradually develop to a permanent fault after its first occurrence. Before a permanent fault occurs in the power system, immediate detection of the incipient fault would help increase system reliability, prevent equipment failure, and reduce the associated cost. This thesis proposes a hybrid method for incipient faults detection and classification. The proposed method firstly diagnoses unique features extracted from real-system measurement data recorded by power quality monitors in the high-voltage substations. Then, a supervised learning technique called support vector machine (SVM) is applied to classify various types of incipient faults. Test results show that the proposed method contributes relatively accurate classification of incipient faults and can be adopted as a useful tool for smart grid applications.