Energy Dissipation and Entropy Generation During the Fatigue Degradation: Application to Health Monitoring of Composites

In this dissertation, an experimental approach for characterizing energy dissipation and degradation evolution in a woven Epoxy/Glass (G10/FR4) laminate subjected to fully reversed bending fatigue test is presented. Infrared thermography and acoustic emission are utilized to characterize the degradation progression. The results show similar evolutionary response indicating the presence of three degradation stages. The effect of the surface cooling on the fatigue life of the laminates is investigated both experimentally and analytically. The results show that the life of the laminate is highly dependent on the temperature and that surface cooling can significantly increase the fatigue life of the laminate. The signatures of acoustic emission (AE) response emanating from laminates are studied. The distribution of the cumulative AE amplitude is described by a power law. Examination of the evolution of the probability density function (PDF) of the AE energy (counts) reveals two scaling zones wherein the transition from the low energy (count) to high energy (count) regime is identified. The low-energy phase represents very low damage state of the laminate characterized by a power law. The AE energy release and counts follow the statistics and power laws that do not depend on the operational conditions. A fatigue damage detection method for the laminates based on the cumulative information entropy is reported. The cumulative entropy demonstrates a persistent trend of nonlinear damage evolution typically observed in the experimental measures of the damage in composite materials. In this dissertation, a continuum formulation for irreversible energy dissipation that accounts for generated acoustic emissions during the loading of the materials is also developed. The evolution of the dissipative energy for AL6061 specimens is experimentally measured as the material is degraded. A statistically similar behavior is observed in different forms of the dissipated energy as the material degrade. Finally, a damage detection method for detection of wear in thrust ball bearings coated with molybdenum disulphide (MoS2) is presented. It employs an energy feature obtained from time-frequency representation of the vibration signal. Extensive experimental studies are conducted to verify the efficacy of the proposed method for fault diagnosis of MoS2 coating

Condition Monitoring of Slow Speed Rotating Machinery Using Acoustic Emission Technology

Slow speed rotating machines are the mainstay of several industrial applications worldwide. They can be found in paper and steel mills, rotating biological contractors, wind turbines etc. Operational experience of such machinery has not only revealed the early design problems but has also presented opportunities for further significant improvements in the technology and economics of the machines. Slow speed rotating machinery maintenance, mostly related to bearings, shafts and gearbox problems, represents the cause of extended outages. Rotating machinery components such as gearboxes, shafts and bearings degrade slowly with operating time. Such a slow degradation process can be identified if a robust on-line monitoring and predictive maintenance technology is used to detect impending problems and allow repairs to be scheduled. To keep machines functioning at optimal levels, failure detection of such vital components is important as any mechanical degradation or wear, if is not impeded in time, will often progress to more serious damage affecting the operational performance of the machine. This requires far more costly repairs than simply replacing a part. Over the last few years there have been many developments in the use of Acoustic Emission (AE) technology and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on slow speed rotating machinery. Unlike conventional technologies such as thermography, oil analysis, strain measurements and vibration, AE has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This programme of research involves laboratory tests for monitoring slow speed rotating machinery components (shafts and bearings) using AE technology. To implement this objective, two test rigs have been designed to assess the capability of AE as an effective tool for detection of incipient defects within low speed machine components (e.g. shafts and bearings). The focus of the experimental work will be on the initiation and growth of natural defects. Further, this research work investigates the source characterizations of AE signals associated with such bearings whilst in operation. It is also hoped that at the end of this research program, a reliable on-line monitoring scheme used for slow speed rotating machinery components can be developed

Vibro-acoustic Analysis of Reciprocating Compressor in the Context of Fault Diagnosis

This project assessed the behaviour of a positive displacement type of compressor utilising airborne acoustic signatures. The study concentrated on finding an improved method based on airborne sound that can be suitable for diagnosing some common faults in reciprocating compressor (RC). Being a critical component of the industry, the condition monitoring of reciprocating compressor is very much needed to avoid any failure of its machine parts that can cause a sudden breakdown of RC. The compressor acoustic signal is a result of various mechanical forces related to the varied cylinder pressure, valve movement, turbulence air flow which in terms contribute to the periodic excitation along with the non-linearity caused by the valve fluttering, hence making the airborne signal complex and non-stationary in nature. The transient response due to the periodic impact of the valves, modulation effect due to the fluid-mechanical interaction and low signal to noise ratio (SNR) are the challenging aspects of this study. To demonstrate the vibro-acoustic property of the reciprocating compressor, first a model was developed. The leakages in valve and intercooler are very common in RC. The second most common fault which is often neglected is a clogged filter. Hence, taking into consideration, filter blockage fault is introduced for the first time in the existing test set up. Three faults (discharge valve leakage, intercooler leakage and filter blockage) are simulated, and corresponding acoustic responses are recorded for further study of the signal-nature. The model is then validated by the actual data from RC test bed. Along with the modelling of compressor acoustics, various signal processing techniques like Minimum Entropy Deconvolution (MED), Teager Energy Operator (TEO) are used on the test data to detect abnormalities present. MED in this case, is proved to be effective in finding the transient responses whereas, TEO serves as an energy detection tool for tracking the total mechanical energy. Still both methods find it difficult to come up with the best possible diagnosis results as they fail to take all the major characteristics of the RC acoustics into consideration. To overcome this challenge, higher order spectral analysis as a form of Modulation Signal Bi-Spectrum (MSB) is used to find out the most effective modulating components by enhancing the modulating characteristics and suppressing the noise. Moreover, the quadratic phase coupling allows MSB to handle the non-linearity that might be present in RC due to the valve fluttering. The proposed MSB based method not only provides a more consistent and accurate diagnosis of compressor faults but also shows that airborne acoustics has a good aspect in fault identification of RC by validating both model and test results. Recognizing that there is perpetual room for improvement, the performance of the proposed RC fault diagnosis method can be enhanced by incorporating a denoising technique developed using the Variational Mode Decomposition (VMD) associated with Kalman filtering method. The future study must also consider several other individual and compound faults that can be incorporated in the study for understanding vibro-acoustic phenomena of RC

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis propos- es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW- FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition opera- tion and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisa- tion; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the lo- calisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presente

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