Model based techniques for automated condition monitoring of HVAC systems have been under development for some years. Results from the application of these methods to systems installed in real buildings have highlighted robustness and sensitivity issues. The generation of false alarms has been identified as a principal factor affecting the potential usefulness of condition monitoring in HVAC applications. The robustness issue is a direct result of the uncertain measurements and the lack of experimental control that axe characteristic of HVAC systems. This thesis investigates the uncertainties associated with implementing a condition monitoring scheme based on simple first principles models in HVAC subsystems installed in real buildings. The uncertainties present in typical HVAC control system measurements are evaluated. A sensor validation methodology is developed and applied to a cooling coil subsystem installed in a real building. The uncertainty in steady-state analysis based on transient data is investigated. The uncertainties in the simplifications and assumptions associated with the derivation of simple first principles based models of heat-exchangers are established. A subsystem model is developed and calibrated to the test system. The relationship between the uncertainties in the calibration data and the parameter estimates are investigated. The uncertainties from all sources are evaluated and used to generate a robust indication of the subsystem condition. The sensitivity and robustness of the scheme is analysed based on faults implemented in the test system during summer, winter and spring conditions.

In recent years, Condition Monitoring (CM), which can be performed via several sensor channels, has been recognized as an effective paradigm for failure prevention of operational equipment or processes. However, the complexity caused by asynchronous data collection with different and/or time-varying sampling/transmission rates has long been a hindrance in the effective use of multichannel data in constructing empirical models. The problem becomes more challenging when sensor readings are incomplete. Traditional sensor data recovery techniques are often prohibited in asynchronous CM environments, not to mention sparse datasets. The proposed Functional Principal Component Analysis (FPCA) methodologies, e.g., nonparametric FPC model and semi-parametric functional regression model, provide new sensor data recovery techniques to improve the reliability and robustness of multichannel CM systems. Based on the FPCA results obtained from historical asynchronous data, the deviation from the smoothing trajectory of each sensor signal can be described by a set of unit-specific model parameters. Furthermore, the relationships among these sensor signals can be identified and used to construct regression models for the correlated signals. For real-time or online implementation, use of these models along with the parameters adjusted by real-time CM data become powerful tools for dealing with asynchronous CM data while recovering lost data when needed. To improve the robustness and predictability in dealing with asynchronous data, which may be skewed in probability distribution, robust methods were developed based on Functional Data Analysis (FDA) and Local Quantile Regression (LQR) models. Case studies examining turbofan aircraft engines and an experimental two-tank flow-control loop are used to demonstrate the effectiveness and adaptability of the proposed sensor data recovery techniques. The proposed methods may also find a variety of applications in systems of other industries, such as nuclear power plants, wind turbines, railway systems, economic fields, etc., which may face asynchronous sampling and/or missing data collection problems.

Induction motors are reliable and widely used in industrialised nations. However induction motors, like any other machine, will eventually fail. If the failure is not anticipated, it can result in a significant revenue loss. Therefore, there is a strong need to develop an efficient maintenance program. The most cost-effective solution is condition-based maintenance. An effective condition-based maintenance program requires an on-line condition monitoring system that can diagnose the condition of an induction motor in order to determine the types of faults and their severity while the motor is under a normal operating condition. The work in this thesis investigates the detection of stator and rotor faults (i.e. shorted turn faults, eccentricity faults, and broken rotor bar faults) using three types of sensor signals (i.e. current, leakage flux, and vibration) under different loading conditions. The work is based on an extensive series of sensor measurements taken using a number of nominally identical healthy machines (2.2 kW) and custom-modified machines (2.2 kW) with configurable stator and rotor fault settings. The thesis starts by investigating the estimation of rotor speed and rotor slot number. These two parameters are important in determining the fault frequency components that are used for detecting the stator and rotor faults. The rotor speed investigation compares four different estimation methods from the three different sensor signal types. It is found that the speed estimation techniques based on the eccentricity harmonics and the rotor frequency in the stator current, the axial leakage flux, and the motor vibration sensor signals can detect the rotor speed very accurately even when the load is as low as 2%. Similarly, this thesis proposes three different rotor slot number estimation techniques from the three different types of sensors and demonstrates that all three techniques can estimate the rotor slot number accurately. In addition, it is shown that the reliability of the estimation techniques can be increased significantly when the three techniques are combined. The shorted turn investigation in this thesis examines and compares potential shorted turn features in the three sensor signal types under five different fault severities and ten different loading conditions. The useful shorted turn features are identified in the thesis, and then examined against variations between the healthy machines in order to determine the loads and the fault severities in which the feature can reliably detect the faults. The results show that the feature based on the EPVA (extended Park’s vector approach) is the best method. This feature can detect turn to turn faults with a severity of 3.5% or greater at loads greater than 20% and phase to phase turn faults with a severity of 1.7% or greater under all loading conditions. However, estimating the fault severity is generally found to be difficult. The thesis also examines the feasibility of detecting static eccentricity faults using the different types of sensor signals under ten different loading conditions. The thesis compares potential eccentricity features under nine different fault severities. The useful features are identified and then combined through weighted linear combination (WLC) in order to produce a better eccentricity fault indicator. The indicator begins to show significant magnitude variation when the fault severity is greater than or equal to 25% and the load is greater than or equal to 25%. The experimental results show that detecting the static eccentricity faults is possible but estimating the fault severity may be difficult. Furthermore, the effects of misalignment faults on the useful eccentricity features are investigated. In this thesis, the analysis of broken rotor bar faults is performed under motor starting and rundown operation. The starting analysis introduces a new approach to detect broken rotor bar faults that utilises the wavelet transform of the envelope of the starting current waveform. The results of the wavelet transform are then processed in order to develop a normalised parameter, called the wavelet indicator. It is found that the wavelet indicator can detect a single broken bar under all loading conditions during motor starting operation. The indicator also increases its magnitude as the severity of the fault increases. On the other hand, the rundown analysis proposes several broken rotor bar fault detection techniques which utilise the induced voltage in the stator windings and the stator magnetic flux linkage after supply disconnection. The experimental results show that detecting the faults during rundown is generally difficult. However, the wavelet approach, which is based on monitoring changes in the motor torque for a given slip, seems to give the best result. / Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2008

With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.

Orientador: Aparecido Carlos Gonçalves / Resumo: A falha catastrófica de caixas de engrenagens acarreta em perdas de produção e custos de manutenção. O elemento mecânico que mais falha em uma caixa de engrenagens é o próprio par engrenado. Estas falhas geralmente ocorrem devido a defeitos pontuais nos dentes como desgaste severo e presença de trincas, contrariando os fatores de segurança previamente definidos por normas referentes aos critérios de falhas em engrenagens. O monitoramento da condição do par engrenado busca avaliar parâmetros representativos dos mecanismos de falha do par engrenado. As técnicas de monitoramento da condição mais utilizadas são a análise de vibrações e análise de lubrificantes. Este trabalho realiza uma revisão bibliográfica de técnicas de monitoramento da condição. A parte experimental consiste na avaliação de uma bancada sob três condições: desgaste severo ao longo da vida útil da engrenagem; engrenagem entalhada para simulação de trinca; engrenagem com variação do entalhe para simulação de uma propagação de trinca. A condição da bancada foi avaliada utilizando técnicas de tratamento de sinais de vibração como TSA, sinal residual, demodulação temporal e análise estatística via PDF beta e; técnicas de análise de lubrificantes como contagem de partículas e espectrometrias de raios-x e infravermelho. Todas as técnicas se mostraram adequadas na avaliação da evolução do desgaste excetuando a espectrometria de infravermelho. Apenas as técnicas de vibração se mostraram adequadas para identificar a pre... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The catastrophic failure of gearboxes results in production losses and maintenance costs. The mechanical component that most fails in gearboxes are the gears. These failures usually occur before the end of useful life projected by criteria of failure standards due teeth defects as severe wear and cracking. The condition monitoring of gearboxes evaluates parameters which can indicate the mechanism of failure in process in the gear. The most commonly used monitoring techniques of gearboxes are vibration analysis and lubricant analysis. Firstly, this work performs a bibliographic review of condition monitoring techniques. The experimental analysis consists of the evaluation of an experimental workbench under three conditions: severe wear throughout the life of the gear; notched gear for crack simulation and; gear with variation of notch for simulation of a crack propagation. The workbench condition was evaluated using vibration signal treatment techniques such as TSA, Residual Signal, Demodulation, Statistical Moments, Crest Factor and Statistical Analysis using PDF beta and; techniques for analyzing lubricants such as particle counting and x-ray and infrared spectrometry. All the techniques were adequate to evaluate the evolution of wear except infrared spectrometry. Only the vibration techniques were adequate to identify the presence an evolution of the notch. Statistical analysis using PDF beta was useful to identify the degradation of a tooth as the notch size evolved. / Mestre

La plupart des méthodes de surveillance des systèmes sont basées sur l'analyse et la caractérisation de grandeurs physiques qui sont par nature tridimensionnelles. Tracées dans un repère euclidien à trois dimensions, ces grandeurs parcourent en fonction du temps une trajectoire dont les caractéristiques géométriques sont représentatives de l'état du système surveillé. Les techniques classiques de surveillance des systèmes étudient les grandeurs mesurées composante par composante, sans prendre en compte leur nature tridimensionnelle et les propriétés géométriques de leur trajectoire. Une part importante de l'information est ainsi ignorée. Dans le cadre de ce travail de recherche, on se propose de développer une méthode d'analyse et de traitement de grandeurs à trois composantes permettant de mettre en évidence les spécificités géométriques des données et de fournir une information complémentaire pour la surveillance des systèmes. La méthode proposée a été appliquée à deux cas différents : la surveillance des creux de tension des réseaux de puissance triphasés et la surveillance des défauts de roulement des machines électriques tournantes. Dans ces deux cas, les résultats obtenus sont prometteurs et montrent que les indicateurs géométriques estimés mènent à de l'information complémentaire qui peut être utile pour la surveillance des systèmes. / Most methods for condition monitoring are based on the analysis and characterization of physical quantities that are three-dimensional in nature. Plotted in a three-dimensional Euclidean space as a function of time, these quantities follow a trajectory whose geometric characteristics are representative of the state of the monitored system. Usual techniques of condition monitoring study the measured quantities component by component, without taking into account their three-dimensional nature and the geometric properties of their trajectory. A significant part of the information is thus ignored. In this research work, we would therefore like to develop a method for the analysis and processing of three-component quantities capable of highlighting the special geometric features of such data and providing complementary information for condition monitoring. The proposed method has been applied to two different cases: voltage dips monitoring in three-phase power networks and bearing faults monitoring in rotating electrical machines. In this two cases, the results obtained are promising and show that the estimated geometric indicators lead to complementary information that can be useful for condition monitoring.

Cette thèse propose une nouvelle méthode pour modéliser les fonctions non linéaires de modulations d’amplitude et de fréquence de signaux multicomposantes non stationnaires de durée longue. La méthode repose sur une décomposition du signal en segments courts pour une modélisation locale sur les segments. Pour initialiser la modélisation, nous avons conçu une première étape qui peut être considérée comme un estimateur indépendant et non paramétrique des fonctions de modulations. L’originalité de l’approche réside dans la définition d’une matrice de convergence totale intégrant simultanément les valeurs d’amplitude et de fréquence et utilisé pour l’association d’un pic à une composante selon un critère d’acceptation stochastique. Suite à cette initialisation, la méthode estime les fonctions de modulations par l'enchaînement des étapes de segmentation, modélisation et fusion. Les fonctions de modulations estimées localement par maximum de vraisemblance sont connectées dans l'étape de fusion, qui supprime les discontinuités, et produit l’estimation globale sur la durée totale du signal. Les étapes sont conçues afin de pouvoir modéliser des signaux multicomposantes avec des morts et naissances, ce qui en fait une de ses originalités par rapport aux techniques existantes. Les résultats sur des signaux réels et simulés ont illustré les bonnes performances et l’adaptabilité de la méthode proposée. / In this thesis, a novel method is proposed for modeling the non-linear amplitude and frequency modulations of non-stationary multi-component signals of long duration. The method relies on the decomposition of the signal into short time segments to carry out local modelings on these segments. In order to initialize the modeling, a first step is designed which can be considered as an independent estimator of the modulations over the entire duration of the signal. The originality of this approach lies in the definition of the total divergence matrix integrating simultaneously the amplitude and frequency values, which are employed for the association of a peak to a component according to a stochastic acceptation criteria. Following the initialization, the proposed method estimates the modulations by the step sequence of segmentation, modeling and fusion. The locally obtained modulation functions estimated by maximum likelihood are finally connected in the fusion step which suppresses their discontinuity and yields the global estimation over the entire signal duration. All these steps are defined in order to be able to model multicomponent signals with births and deaths, making one of its original features compared to existing techniques. The results on real and simulated signals have shown the good performance and adaptability of the proposed method.

Submitted by RODOLFO DE SOUSA SANTOS null (rodolfosousa4@gmail.com) on 2017-08-24T18:31:09Z No. of bitstreams: 1 TESE _RODOLFO_CORRIGIDA_19_08_2017_Final.pdf: 4285264 bytes, checksum: b5dac391b40121a31b55502fba5c1c43 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-08-25T16:18:27Z (GMT) No. of bitstreams: 1 santos_rs_dr_guara.pdf: 4285264 bytes, checksum: b5dac391b40121a31b55502fba5c1c43 (MD5) / Made available in DSpace on 2017-08-25T16:18:27Z (GMT). No. of bitstreams: 1 santos_rs_dr_guara.pdf: 4285264 bytes, checksum: b5dac391b40121a31b55502fba5c1c43 (MD5) Previous issue date: 2017-07-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os sinais de vibrações de máquinas rotativas conduzem a informações dinâmicas da máquina e esta análise é de grande importância no que diz respeito ao monitoramento de condição e diagnósticos de máquinas. Vários métodos de análises têm sido empregados no sentido de diagnosticar falhas em componentes de máquinas tais como engrenagens, rolamentos, dentre outros. Este trabalho apresenta uma análise sobre detecção de falhas em rolamentos de máquinas rotativas, e para esta apreciação utilizou-se os bancos de dados da CASE WESTERN RESERV UNIVERSITY e o banco de dados da FEG/UNESP. O objetivo principal deste trabalho foi a implementação de técnicas avançadas para identificar e caracterizar as falhas que são geradas em rolamentos, vislumbrando o aprimoramento da manutenção baseada na condição. Inicialmente, realizou-se a implementação e simulação no banco de dados da (CWRU), utilizando o software MATLAB e por meio da técnica de ressonância de alta frequência (HFRT), obteve-se resultados satisfatórios, entretanto esta metodologia é limitada uma vez que ela é empregada apenas para regime estacionário. A implementação da técnica HFRT não identificou em alguns casos a frequências para caracterização dos defeitos nas pistas dos rolamentos. Em seguida, utilizou-se a técnica Short Time Fourier Transform-STFT. A implementação proporcionou uma análise bem mais sensível aos impactos gerados nas pistas, pois, com a utilização da STFT, foi possível identificar as frequências características de defeitos. Para efeito de comparação optou-se por utilizar a técnica Wavelet combinada com a técnica do envelope. Esta análise foi aplicada usando a Wavelet Daubechies de ordem 4 (db4), em cuja implementação, realizou-se a decomposição do sinal de um rolamento com defeito e verificou-se qual destes apresentou o maior nível RMS e selecionou-se este sinal, pois o mesmo é o nível ideal para aplicação do método. Realizou-se a mesma apreciação ao banco de dados da FEG/UNESP. A análise realizada da técnica de Wavelet combinada com a técnica HFRT foi a que demonstrou melhor capacidade em relação às técnicas HFRT e STFT. Em seguida realizou-se a implementação da técnica de curtose espectral associada à técnica do envelope foi a que proporcionou os resultados mais precisos e satisfatórios, pois com a aplicação dessa metodologia foi possível a determinação de forma automática da região de ressonância e consequentemente uma melhora na caracterização das frequências de defeitos observadas nos rolamentos dos experimentos realizados em máquinas rotativas. / The vibration signals from rotating machines provide a set of dynamic information, which are very important for continuous condition monitoring of machinery. Several analytical methods have been employed in order to diagnose faults in machines components such as gears, bearings and others. This paper presents a fault detection analysis of rotating machinery bearings, using data from CASE WESTERN UNIVERSITY RESERVOIR and the FEG / UNESP database. The main objective of this work is the implementation of advanced techniques to identify and characterize bearing failures, with the purpose to improve maintenance under working conditions. At first, the implementation and simulation were done with data extracted from the database of (CWRU) using MATLAB software and high-frequency resonance technique (HFRT), which led to satisfactory results. However, this technique is limited since it is used only in a stationary regime. In some cases, the implementation of HFRT technique was not able to identify the defect frequencies of the bearing’s races. Next the STFT Short-Time Fourier Transform technique was used. Its implementation provided a much more sensitive analysis of the impacts on the slopes; using STFT allowed to identify the characteristic defect frequencies. For comparison purposes, the wavelet technique combined with the envelope technique were used. This analysis was applied using Daubechies Wavelet of order 4 (DB4). In its implementation, a defective bearing signal was decomposed into various parts. The signal part with the highest RMS level was selected, because it provides best conditions for applying the method. Analogously, data from the FEG / UNESP database were treated. The Wavelet analysis technique combined with HFRT technique demonstrated better capability with respect to the HFRT and STFT techniques. The implementation of the spectral kurtosis technique associated with the envelope technique provided the most accurate and satisfactory results, since with the application of this methodology it was possible to determine the resonance region automatically. Consequently, this is an improvement regarding the characterization of the defect frequencies of the bearings observed in experiments with rotating machinery.

Submitted by CARLOS EDUARDO SGOTTI (carlos.sgotti@hotmail.com) on 2018-04-27T18:42:48Z No. of bitstreams: 1 2018_Sgotti_Dissertação_Mestrado.pdf: 10994360 bytes, checksum: 90aa52990d9db0b33dd9950ddfb58289 (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-04-27T19:11:29Z (GMT) No. of bitstreams: 1 sgotti_ce_me_ilha.pdf: 10994360 bytes, checksum: 90aa52990d9db0b33dd9950ddfb58289 (MD5) / Made available in DSpace on 2018-04-27T19:11:29Z (GMT). No. of bitstreams: 1 sgotti_ce_me_ilha.pdf: 10994360 bytes, checksum: 90aa52990d9db0b33dd9950ddfb58289 (MD5) Previous issue date: 2018-02-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A falha catastrófica de caixas de engrenagens acarreta em perdas de produção e custos de manutenção. O elemento mecânico que mais falha em uma caixa de engrenagens é o próprio par engrenado. Estas falhas geralmente ocorrem devido a defeitos pontuais nos dentes como desgaste severo e presença de trincas, contrariando os fatores de segurança previamente definidos por normas referentes aos critérios de falhas em engrenagens. O monitoramento da condição do par engrenado busca avaliar parâmetros representativos dos mecanismos de falha do par engrenado. As técnicas de monitoramento da condição mais utilizadas são a análise de vibrações e análise de lubrificantes. Este trabalho realiza uma revisão bibliográfica de técnicas de monitoramento da condição. A parte experimental consiste na avaliação de uma bancada sob três condições: desgaste severo ao longo da vida útil da engrenagem; engrenagem entalhada para simulação de trinca; engrenagem com variação do entalhe para simulação de uma propagação de trinca. A condição da bancada foi avaliada utilizando técnicas de tratamento de sinais de vibração como TSA, sinal residual, demodulação temporal e análise estatística via PDF beta e; técnicas de análise de lubrificantes como contagem de partículas e espectrometrias de raios-x e infravermelho. Todas as técnicas se mostraram adequadas na avaliação da evolução do desgaste excetuando a espectrometria de infravermelho. Apenas as técnicas de vibração se mostraram adequadas para identificar a presença do entalhe. A análise estatística via PDF beta se mostrou útil para identificar a degradação de um dente conforme evolui o tamanho do entalhe. / The catastrophic failure of gearboxes results in production losses and maintenance costs. The mechanical component that most fails in gearboxes are the gears. These failures usually occur before the end of useful life projected by criteria of failure standards due teeth defects as severe wear and cracking. The condition monitoring of gearboxes evaluates parameters which can indicate the mechanism of failure in process in the gear. The most commonly used monitoring techniques of gearboxes are vibration analysis and lubricant analysis. Firstly, this work performs a bibliographic review of condition monitoring techniques. The experimental analysis consists of the evaluation of an experimental workbench under three conditions: severe wear throughout the life of the gear; notched gear for crack simulation and; gear with variation of notch for simulation of a crack propagation. The workbench condition was evaluated using vibration signal treatment techniques such as TSA, Residual Signal, Demodulation, Statistical Moments, Crest Factor and Statistical Analysis using PDF beta and; techniques for analyzing lubricants such as particle counting and x-ray and infrared spectrometry. All the techniques were adequate to evaluate the evolution of wear except infrared spectrometry. Only the vibration techniques were adequate to identify the presence an evolution of the notch. Statistical analysis using PDF beta was useful to identify the degradation of a tooth as the notch size evolved.

Monitoring wear particles in lubricating oils allows specialists to evaluate thehealth and functionality of a mechanical system. The main analysis techniquesavailable today are manual particle analysis and automatic optical analysis. Man-ual particle analysis is effective and reliable since the analyst continuously seeswhat is being counted . The drawback is that the technique is quite time demand-ing and dependent of the skills of the analyst. Automatic optical particle countingconstitutes of a closed system not allowing for the objects counted to be observedin real-time. This has resulted in a number of sources of error for the instrument.In this thesis a new method for counting particles based on light microscopywith image analysis is proposed. It has proven to be a fast and effective methodthat eliminates the sources of error of the previously described methods. Thenew method correlates very well with manual analysis which is used as a refer-ence method throughout this study. Size estimation of particles and detectionof metallic particles has also shown to be possible with the current image analy-sis setup. With more advanced software and analysis instrumentation, the imageanalysis method could be further developed to a decision based machine allowingfor declarations about which wear mode is occurring in a mechanical system.