1,013 research outputs found
A new method of vibration analysis for the diagnosis of impeller in a centrifugal pump
Centrifugal pumps are widely used in many important industries such as power generation plants, chemical processes and petroleum refiners. The condition monitoring of centrifugal pumps is highly regarded by many researchers and users to minimize unexpected break-downs. Impellers are the core parts of pumps but often appear early damages due to flow cav-itation and erosion. This paper investigates a new approach to monitoring the conditions of impellers using surface vibration with advanced signal analysis. As overall vibration respons-es contain high level of broadband noises due to cavities and turbulences, noise reduction is critical to develop reliable and effective features. However, considering the modulation effect between rotating shaft and blade passing components, a modulation signal bispectrum (MSB) method is employed to extract these deterministic characteristics of modulations, which is different from previous researches in that broadband random sources are often used. Experi-mental results show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions
Influence of Auditory Cues on the Neuronal Response to Naturalistic Visual Stimuli in a Virtual Reality Setting
Virtual reality environments offer great opportunities to study the performance of brain-computer interfaces (BCIs) in real-world contexts. As real-world stimuli are typically multimodal, their neuronal integration elicits complex response patterns. To investigate the effect of additional auditory cues on the processing of visual information, we used virtual reality to mimic safety-related events in an industrial environment while we concomitantly recorded electroencephalography (EEG) signals. We simulated a box traveling on a conveyor belt system where two types of stimuli – an exploding and a burning box – interrupt regular operation. The recordings from 16 subjects were divided into two subsets, a visual-only and an audio-visual experiment. In the visual-only experiment, the response patterns for both stimuli elicited a similar pattern – a visual evoked potential (VEP) followed by an event-related potential (ERP) over the occipital-parietal lobe. Moreover, we found the perceived severity of the event to be reflected in the signal amplitude. Interestingly, the additional auditory cues had a twofold effect on the previous findings: The P1 component was significantly suppressed in the case of the exploding box stimulus, whereas the N2c showed an enhancement for the burning box stimulus. This result highlights the impact of multisensory integration on the performance of realistic BCI applications. Indeed, we observed alterations in the offline classification accuracy for a detection task based on a mixed feature extraction (variance, power spectral density, and discrete wavelet transform) and a support vector machine classifier. In the case of the explosion, the accuracy slightly decreased by –1.64% p. in an audio-visual experiment compared to the visual-only. Contrarily, the classification accuracy for the burning box increased by 5.58% p. when additional auditory cues were present. Hence, we conclude, that especially in challenging detection tasks, it is favorable to consider the potential of multisensory integration when BCIs are supposed to operate under (multimodal) real-world conditions
Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems
A thesis submitted to the Faculty of Engineering and the Built Environment,
University of the Witwatersrand, Johannesburg, in ful lment of the requirements for
the degree of Doctor of Philosophy.
Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with
the resulting loss of speech. With recent advances in portable computing power,
automatic lip-reading (ALR) may become a viable approach to voice restoration. This
thesis addresses the image processing aspect of ALR, and focuses three contributions
to colour-based lip segmentation.
The rst contribution concerns the colour transform to enhance the contrast
between the lips and skin. This thesis presents the most comprehensive study to
date by measuring the overlap between lip and skin histograms for 33 di erent
colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%,
and results show that selecting the correct transform can increase the segmentation
accuracy by up to three times.
The second contribution is the development of a new lip segmentation algorithm
that utilises the best colour transforms from the comparative study. The algorithm
is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation
error (SE) of 7:39 %.
The third contribution focuses on the impact of the histogram threshold on the
segmentation accuracy, and introduces a novel technique called Adaptive Threshold
Optimisation (ATO) to select a better threshold value. The rst stage of ATO
incorporates -SVR to train the lip shape model. ATO then uses feedback of shape
information to validate and optimise the threshold. After applying ATO, the SE
decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp
or relative improvement of 15:1%. While this thesis concerns lip segmentation in
particular, ATO is a threshold selection technique that can be used in various
segmentation applications.MT201
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 conditionÂbased 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 energÃa eólica es una de las principales energÃas 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 perÃodos 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 desafÃo. 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 entropÃa 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 entropÃa 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 entropÃa 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 energÃa 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 energÃa 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 perÃodes 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 mecà nic i baixes velocitats de rotació. A causa de que aquestes condicions d’operació impacten negativament en els resultats dels mètodes i tècniques està ndard 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 clà ssics 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’anà lisi 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
3D reconstruction and motion estimation using forward looking sonar
Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains
including archaeology, oil and gas industry, coral reef monitoring, harbour’s security,
and mine countermeasure missions. As electromagnetic signals do not penetrate
underwater environment, GPS signals cannot be used for AUV navigation, and optical
cameras have very short range underwater which limits their use in most underwater
environments.
Motion estimation for AUVs is a critical requirement for successful vehicle recovery
and meaningful data collection. Classical inertial sensors, usually used for AUV motion
estimation, suffer from large drift error. On the other hand, accurate inertial sensors are
very expensive which limits their deployment to costly AUVs. Furthermore, acoustic
positioning systems (APS) used for AUV navigation require costly installation and
calibration. Moreover, they have poor performance in terms of the inferred resolution.
Underwater 3D imaging is another challenge in AUV industry as 3D information is
increasingly demanded to accomplish different AUV missions. Different systems have
been proposed for underwater 3D imaging, such as planar-array sonar and T-configured
3D sonar. While the former features good resolution in general, it is very expensive and
requires huge computational power, the later is cheaper implementation but requires
long time for full 3D scan even in short ranges.
In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by
proposing relatively affordable methodologies and study different parameters affecting
their performance. We introduce a new motion estimation framework for AUVs which
relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an
Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on
forward looking sonars; the proposed system features cheaper implementation than
planar array sonars and solves the delay problem in T configured 3D sonars
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