Classification of partial discharge EMI conditions using permutation entropy-based features

In this paper we investigate the application of feature extraction and machine learning techniques to fault identification in power systems. Specifically we implement the novel application of Permutation Entropy-based measures known as Weighted Permutation and Dispersion Entropy to field Electro- Magnetic Interference (EMI) signals for classification of discharge sources, also called conditions, such as partial discharge, arcing and corona which arise from various assets of different power sites. This work introduces two main contributions: the application of entropy measures in condition monitoring and the classification of real field EMI captured signals. The two simple and low dimension features are fed to a Multi-Class Support Vector Machine for the classification of different discharge sources contained in the EMI signals. Classification was performed to distinguish between the conditions observed within each site and between all sites. Results demonstrate that the proposed approach separated and identified the discharge sources successfully

Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features

Electro-Magnetic Interference (EMI) is a measurement technique for Partial Discharge (PD) signals which arise in operating electrical machines, generators and other auxiliary equipment due to insulation degradation. Assessment of PD can help to reduce machine downtime and circumvent high replacement and maintenance costs. EMI signals can be complex to analyze due to their nonstationary nature. In this paper, a software condition-monitoring model is presented and a novel feature extraction technique, suitable for nonstationary EMI signals, is developed. This method maps multiple discharge sources signals, including PD, from the time domain to a feature space which aids interpretation of subsequent fault information. Results show excellent performance in classifying the different discharge sources

Entropy-based feature extraction for electromagnetic discharges classification in high-voltage power generation

This work exploits four entropy measures known as Sample, Permutation, Weighted Permutation, and Dispersion Entropy to extract relevant information from Electromagnetic Interference (EMI) discharge signals that are useful in fault diagnosis of High-Voltage (HV) equipment. Multi-class classification algorithms are used to classify or distinguish between various discharge sources such as Partial Discharges (PD), Exciter, Arcing, micro Sparking and Random Noise. The signals were measured and recorded on different sites followed by EMI expert’s data analysis in order to identify and label the discharge source type contained within the signal. The classification was performed both within each site and across all sites. The system performs well for both cases with extremely high classification accuracy within site. This work demonstrates the ability to extract relevant entropy-based features from EMI discharge sources from time-resolved signals requiring minimal computation making the system ideal for a potential application to online condition monitoring based on EMI

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Diagnosis of low-speed bearings via vibration-based entropy indicators and acoustic emissions

Tesi del Pla de doctorat industrial de la Generalitat de Catalunya. Tesi en modalitat compendi de publicacions, amb diferents seccions retallades per drets dels editorsWind energy is one ofthe main renewable energies to replace fossil fuels in the generation of electricityworldwide. To enhance and accelerate its implementation at a large scale, it is vital to reduce the costs associated with maintenance. As com ponent breakages force the turbine to stop for long repair times, the wind industry m ust switch from the old-fashioned preventive or corrective maintenance to condition-based maintenance (also called predictive maintenance). The condition­based maintenance of pitch bearings is especiallychallenging, as the operating conditions include high mechanical stress and low rotational speed. Since these operating conditions im pact negatively on the results of the standard methods and techniques applied in current condition-based monitoring systems, the condition-based maintenance of pitch bearings is still a challenge. Therefore, this thes is is focused on the research of novel methods and techniques that obtain reliable information on the state of pitch bearings for condition-based maintenance. lnitially, the acknowledgment ofthe state ofthe art is performed to recognize the methods and signals. This step endorses the decision to analyze the vibration signals and acoustic emissions throughout this thesis. Due to the particular operating conditions of pitch bearings, this research states the need to create data sets to replicate the particular operating conditions in a controlled laboratory experiment. As a res ult, a datas et based on vibrations, and a second datas et based on acoustic emissions are generated. The vibration datas et allows the validation of a novel algorithm for the low-speed bearing diagnosis, which is based on the concept of entropy by the definition of Shannon and Rényi. In com parison to the classical methods found in the literature, the diagnosis of low-speed bearings based on entropy-based indicators can extract more reliable information. Moreover, the research of the com bination of several indicators to improve the diagnosis revea Is that the entropy-based indicators can extract more information than regular indicators used in academia. The datas et of acoustic emissions from low-speed bearings helps to contribute to the development of methods for diagnosis. In this research, the analysis of the energyfrom the signals reveals a dependencyon the intensityand the presence of damage. In addition, a relation between the waveform ofthe analyzed energy and the existence of damage is em phas ized.La energía eólica es una de las principales energías renovables consideradas para reemplazar los combustibles fósiles en la generación de electricidad a nivel mundial. Para mejorar y acelerar su implementación a gran escala, es vital reducir los costes asociados con el mantenimiento. Como las roturas de los componentes obligan a la turbina a detenerse durante largos períodos de reparación, la industria eólica necesita cambiar del anticuado mantenimiento preventiv o correctivo al mantenimiento basado en la condición (también llamado mantenimiento predictivo). El mantenimiento basado en la condición de los rodamientos pitch es especialmente desafiante, porque las condiciones de operación incluyen un alto estrés mecánico y bajas velocidades de rotación. Debido a que estas condiciones de operación impactan negativamente en los resultados de los métodos y técnicas estándar aplicados en los sistemas actuales de monitoreo basados en el estado, el mantenimiento basado en el estado de los rodamientos pitch sigue siendo un desafío. Por tanto, esta tesis se centra en la investigación de métodos y técnicas novedosas que obtengan información fiable sobre el estado de los rodamientos pitch para el mantenimiento basado en la condición. Inicialmente, se realiza el reconocimiento del estado del arte para reconocer los métodos y señales utilizados. Este paso avala la decisión de analizar las señales de vibración y las emisiones acústicas a lo largo de esta tesis. Debido a las condiciones de funcionamiento particulares de los rodamientos pitch, esta investigación reconoce la necesidad de crear un conjunto de datos para replicar las condiciones de funcionamiento particulares del rodamiento pitch en una experiencia de laboratorio controlado. Como resultado, se genera un conjunto de datos basado en vibraciones y un segundo conjunto de datos basado en emisiones acústicas. El conjunto de datos de vibraciones permite la validación de un algoritmo novedoso para el diagnóstico de rodamientos de baja velocidad, el cual se basa en el concepto de la entropía según la definición de Shannon y Rényi. En comparación con los métodos clásicos que se encuentran en la literatura, el diagnóstico de rodamientos de baja velocidad basado en indicadores basados en la entropía puede extraer información más confiable. Además, la investigación de la combinación de varios indicadores para mejorar el diagnóstico revela que los indicadores basados en la entropía pueden extraer más información que los indicadores habituales utilizados en la academia. El conjunto de datos de las emisiones acústicas de los rodamientos de baja velocidad ayuda a contribuir al desarrollo de métodos de diagnóstico. En esta investigación, el análisis de la energía de las señales revela una dependencia de la intensidad y la presencia de daño. Además, se enfatiza una relación entre la forma de onda de la energía analizada y la existencia de daño.L'energia eòlica és una de les principals energies renovables considerades per reemplaçar els combustibles fòssils en la generació d'electricitat a nivell mundial. Per millorar i accelerar la seva implementació a gran escala, és vital reduir els costos associats amb el manteniment. Com els trencaments dels components obliguen a la turbina a aturar-se durant llargs períodes de reparació, la industria eòlica necessita canviar de l'antiquat manteniment preventiu o correctiu al manteniment basat en la condició (també anomenat manteniment predictiu). El manteniment basat en la condició dels rodaments de pas és especialment desafiant, perquè les condicions d’operació inclouen un alt estrès mecànic i baixes velocitats de rotació. A causa de que aquestes condicions d’operació impacten negativament en els resultats dels mètodes i tècniques estàndard aplicats en els sistemes actuals de monitorització basats en l'estat, el manteniment basat en l'estat dels rodaments de pas segueix sent un desafiament. Per tant, aquesta tesi se centra en la investigació de mètodes i tècniques noves que obtinguin informació fiable sobre l'estat dels rodaments de pas per al manteniment basat en la condició. Inicialment, es realitza el reconeixement de l'estat de l'art per reconèixer els mètodes i senyals utilitzats. Aquest pas avala la decisió d'analitzar els senyals de vibració i les emissions acústiques al llarg d'aquesta tesi. A causa de les condicions de funcionament particulars dels rodaments de pas, aquesta investigació reconeix la necessitat de crear un conjunt de dades per replicar les condicions de funcionament particulars del rodament de pas en un experiment de laboratori controlat. Com a resultat, es genera un conjunt de dades basat en vibracions i un segon conjunt de dades basat en emissions acústiques. El conjunt de dades de vibracions permet la validació d'un algoritme nou per al diagnòstic de rodaments de baixa velocitat, el qual es basa en el concepte de l'entropia segons la definició de Shannon i Renyi. En comparació amb els mètodes clàssics que es troben a la literatura, el diagnòstic de rodaments de baixa velocitat basat en indicadors basats en l'entropia pot extreure informació més fiable. A més, la investigació de la combinació de diversos indicadors per millorar el diagnòstic revela que els indicadors basats en l'entropia poden extreure més informació que els indicadors habituals utilitzats en la literatura. El conjunt de dades de les emissions acústiques dels rodaments de baixa velocitat ajuda a contribuir al desenvolupament de mètodes de diagnòstic. En aquesta investigació, l’anàlisi de l'energia de les senyals revela una dependència de la intensitat i la presència de dany. A més, s'emfatitza una relació entre la forma d'ona de l'energia analitzada i l’existència de dany.Energia eolikoa mundu mailan elektrizitatea sortu eta erregai fosilak ordezkatzeko energia berriztagarri nagusietako bat da. Eskala handiko ezarpena hobetu eta bizkortzeko, ezinbestekoa da mantentze-lanekin lotutako kostuak murriztea. Osagaien hausturek turbina konponketa-aldi luzeetan gelditzera behartzen dutenez, industria eolikoak mantentze-lan prebentibo edo zuzentzaile zaharkitutik egoeran oinarritutako mantentzelanetara aldatu behar du (mantentze-lan prediktiboa ere esaten zaio). Pitch errodamenduen egoeran oinarritutako mantentzea bereziki desa atzailea da, tentsio mekaniko handiak jasaten baitituzte eta errotazio-abiadura txikietan egoten baitira abian. Operaziobaldintza horiek eragin negatiboa dutenez egoeran oinarritutako egungo monitorizazio sistemetan erabiltzen diren metodo eta teknika estandarren emaitzetan, pitch errodamenduen egoeran oinarritutako mantentze-lanak erronka bat izaten jarraitzen du. Tesi hau egoeran oinarritutako mantenurako pitch errodamenduen egoerari buruzko informazio dagarria lortzen duten metodo eta teknika berritzaileen ikerketan oinarritzen da. Hasieran, teknologiaren egungo egoera aztertzen da, erabilitako metodoak eta seinaleak ezagutzeko. Urrats honek tesi honetan zehar bibrazio-seinaleak eta emisio akustikoak aztertzeko erabakia bermatzen du. Pitch errodamenduen funtzionamendu baldintza bereziak direla eta, ikerketa honek adierazten du beharrezkoa dela datu multzo bat sortzea pitch errodamenduaren funtzionamendu baldintza partikularrak erreplikatzeko laborategi kontrolatuko testuinguru batean. Ondorioz, bibrazioetan oinarritutako datu-multzo bat eta emisio akustikoetan oinarritutako bigarren datu-multzo bat sortzen dira. Bibrazioen datu-multzoak abiadura txikiko errodamenduen diagnostikorako algoritmo berritzaile bat baliozkotzea ahalbidetzen du, zeina entropiaren kontzeptuan oinarritzen baita Shannon eta R enyiren de nizioaren arabera. Literaturan dauden metodo klasikoekin alderatuta, entropian oinarritutako adierazleek abiadura txikiko errodamenduen diagnostikorako informazio dagarriagoa atera dezakete. Gainera, diagnostikoa hobetzeko hainbat adierazleren konbinazioaren ikerketak agerian uzten du entropian oinarritutako adierazleek akademian erabiltzen diren ohiko adierazleek baino informazio gehiago atera dezaketela. Abiadura txikiko errodamenduen emisio akustikoen datu multzoak diagnostiko metodoak garatzen laguntzen du. Ikerketa lan honetan, seinaleen energiaren azterketak intentsitatearekiko eta kaltearen presentziarekiko dependentzia adierazten du. Gainera, aztertutako energiaren uhin-formaren eta kaltearen arteko erlazioa nabarmentzen da.Postprint (published version

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

Measurements, Models, Systems and Design

531 s.

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

Articles indexats publicats per investigadors del Campus de Terrassa: 2021

Aquesta pàgina recull els 374 articles publicats per 273 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2021Postprint (author's final draft