Advances in machine condition monitoring technologies are driven by the rise in complexity of modern machines and the increased demand for product reliability. Condition monitoring research tends to focus on the development of signal processing algorithms that are sensitive to machine faults, robust under time-varying operating conditions, and informative regarding the nature and extent of machine faults. A significant challenge remains for monitoring the condition of machines that are subject to time-varying operating conditions. The here presented work is concerned with the development of cost effective condition monitoring algorithms. It is investigated how empirical models (including probability density distributions and regression functions) may be used to extract diagnostic information from machine response signals that have been generated under fluctuating operating conditions. The proposed methodology is investigated on a number of case studies, including gearboxes, alternator end windings, and haul roads. It is shown how empirical models for machine condition monitoring may generally be implemented according to one of two basic approaches. The two approaches are referred to as discrepancy analysis and waveform reconstruction. Discrepancy analysis is concerned with the comparison of a novel signal to a reference model. The reference model is sufficiently expressive to represent vibration response as measured on a healthy machine over a range of operating conditions. The novel signal is compared to the reference model in such a manner that a discrepancy signal transform is obtained. A discrepancy signal is sensitive to faults, robust to time-varying operating conditions, and inherently simple. As such it may further beWaveform reconstruction implements a regression function to model machine response as a function of different state space variables. The regression function may subsequently be exploited to extract diagnostic information. The machine response may for instance be reconstructed at a specified steady state operating condition. This renders the signal wide-sense stationary so that Fourier analysis may be applied. analysed in order to extract periodicities and magnitudes as diagnostic markers. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

This dissertation investigates the effectiveness of a two stage fault identification methodology for rotating machines operating under non-stationary conditions with the use of a single vibration transducer. The proposed methodology transforms the machine vibration signal into a discrepancy signal by means of smart feature selection and statistical models. The discrepancy signal indicates the angular position and relative magnitude of irregular signal patterns which are assumed to be indicative of gear faults. The discrepancy signal is also independent of healthy vibration components, such as the meshing frequency, and effects of fluctuating operating conditions. The use of the discrepancy signal significantly reduces the complexity of fault detection and diagnosis. The first stage of the methodology involves extracting smart instantaneous operating condition specific features, while the second stage requires extracting smart instantaneous fault sensitive features. The instantaneous operating condition features are extracted from the coefficients of the low frequency region of the STFT of the vibration signal, since they are sensitive to operating condition changes and robust to the presence of faults. Then the sequence of operating conditions are classified using a hidden Markov model (HMM). The instantaneous fault features are then extracted from the coefficients in the wavelet packet transform (WPT) around the natural frequencies of the gearbox. These features are the converse to the operating condition features,since they are sensitive to the presence of faults and robust to the fluctuating operating conditions. The instantaneous fault features are sent to a set of Gaussian mixture models (GMMs), one GMM for each identified operating condition which enables the instantaneous fault features to be evaluated with respect to their operating condition. The GMMs generate a discrepancy signal, in the angular domain, from which gear faults may be detected and diagnosed by means of simple analysis techniques. The proposed methodology is validated using experimental data from an accelerated life test of a gearbox operated under fluctuating load and speed conditions. / Dissertation (MEng)--University of Pretoria, 2015. / Mechanical and Aeronautical Engineering / Unrestricted

Vibration-based condition monitoring is a key and crucial element for asset longevity and to avoid unexpected financial compromise. Currently, data-driven methodologies often require significant investments into data acquisition and a large amount of operational data for both healthy and unhealthy cases. The acquisition of unhealthy fault data is often financially infeasible and the result is that most methods detailed in literature are not suitable for critical industrial applications. In this work, unsupervised latent variable models negate the requirement for asset fault data. These models operate by learning the representation of healthy data and utilise health indicators to track deviance from this representation. A variety of latent variable models are compared, namely: Principal Component Analysis, Variational Auto-Encoders and Generative Adversarial Network-based methods. This research investigated the relationship between time-series data and latent variable model design under the sensible notion of data interpretation, the influence of model complexity on result performance on different datasets and shows that the latent manifold, when untangled and traversed in a sensible manner, is indicative of damage. Three latent health indicators are proposed in this work and utilised in conjunction with a proposed temporal preservation approach. The performance is compared over the different models. It was found that these latent health indicators can augment standard health indicators and benefit model performance. This allows one to compare the performance of different latent variable models, an approach that has not been realised in previous work as the interpretation of the latent manifold and the manifold response to anomalous instances had not been explored. If all aspects of a latent variable model are systematically investigated and compared, different models can be analysed on a consistent platform. In the model analysis step, a latent variable model is used to evaluate the available data such that the health indicators used to infer the health state of an asset, are available for analysis and comparison. The datasets investigated in this work consist of stationary and time-varying operating conditions. The objective was to determine whether deep learning is comparable or on par with state-of-the-art signal processing techniques. The results showed that damage is detectable in both the input space and the latent space and can be trended to identify clear condition deviance points. This highlights that both spaces are indicative of damage when analysed in a sensible manner. A key take away from this work is that for data that contains impulsive components that manifest naturally and not due to the presence of a fault, the anomaly detection procedure may be limited by inherent assumptions made in model formulations concerning Gaussianity. This work illustrates how the latent manifold is useful for the detection of anomalous instances, how one must consider a variety of latent-variable model types and how subtle changes to data processing can benefit model performance analysis substantially. For vibration-based condition monitoring, latent variable models offer significant improvements in fault diagnostics and reduce the requirement for expert knowledge. This can ultimately improve asset longevity and the investment required from businesses in asset maintenance. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020. / Eskom Power Plant Engineering Institute (EPPEI) / UP Postgraduate Bursary / Mechanical and Aeronautical Engineering / MEng (Mechanical Engineering) / Unrestricted

First part of this master´s thesis decribes the basic properties of hydrogen, its utilization in industry and economy. Greater attention is focused on the possibilities of hydrogen produciton. Next part describes anaerobic fermentation and optimum conditions for hydrogen production. The main part of the thesis deals with posibility of hydrogen production in an experimental laboratory unit using brewer´s grains as a primary material. The experiment was aimed to find suitable process conditions for hydrogen production. The quantity of produced gas and his quality depending on the amount of brewer´s grains and frequency of the dosage was observed. Based on the experiment, some recommendations are proposed for further research. An automatic feeding system is the most important of them. It will improve the quality of experimental work. A separate chapter is devoted to the automatic feeding system.

Condition monitoring is very important for critical assets such as gearboxes used in the power and mining industries. Fluctuating operating conditions are inevitable for wind turbines and mining machines such as bucket wheel excavators and draglines due to the continuous uctuating wind speeds and variations in ground properties, respectively. Many of the classical condition monitoring techniques have proven to be ine ective under uctuating operating conditions and therefore more sophisticated techniques have to be developed. However, many of the signal processing tools that are appropriate for uctuating operating conditions can be di cult to interpret, with the presence of incipient damage easily being overlooked. In this study, a cost-e ective diagnostic methodology is developed, using machine learning techniques, to diagnose the condition of the machine in the presence of uctuating operating conditions when only an acceleration signal, generated from a gearbox during normal operation, is available. The measured vibration signal is order tracked to preserve the angle-cyclostationary properties of the data. A robust tacholess order tracking methodology is proposed in this study using probabilistic approaches. The measured vibration signal is order tracked with the tacholess order tracking method (as opposed to computed order tracking), since this reduces the implementation and the running cost of the diagnostic methodology. Machine condition features, which are sensitive to changes in machine condition, are extracted from the order tracked vibration signal and processed. The machine condition features can be sensitive to operating condition changes as well. This makes it difficult to ascertain whether the changes in the machine condition features are due to changes in machine condition (i.e. a developing fault) or changes in operating conditions. This necessitates incorporating operating condition information into the diagnostic methodology to ensure that the inferred condition of the machine is not adversely a ected by the uctuating operating conditions. The operating conditions are not measured and therefore representative features are extracted and modelled with a hidden Markov model. The operating condition machine learning model aims to infer the operating condition state that was present during data acquisition from the operating condition features at each angle increment. The operating condition state information is used to optimise robust machine condition machine learning models, in the form of hidden Markov models. The information from the operating condition and machine condition models are combined using a probabilistic approach to generate a discrepancy signal. This discrepancy signal represents the deviation of the current features from the expected behaviour of the features of a gearbox in a healthy condition. A second synchronous averaging process, an automatic alarm threshold for fault detection, a gear-pinion discrepancy distribution and a healthy-damaged decomposition of the discrepancy signal are proposed to provide an intuitive and robust representation of the condition of the gearbox under uctuating operating conditions. This allows fault detection, localisation as well as trending to be performed on a gearbox during uctuating operation conditions. The proposed tacholess order tracking method is validated on seven datasets and the fault diagnostic methodology is validated on experimental as well as numerical data. Very promising results are obtained by the proposed tacholess order tracking method and by the diagnostic methodology. / Dissertation (MEng)--University of Pretoria, 2016. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

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Yes / This work presents a systematic evaluation of the membrane and process characteristics of a scaled-up pressure retarded osmosis (PRO). In order to meet pre-defined membrane economic viability ( ≥ 5 W/m2), different operating conditions and design parameters are studied with respect to the increase of the process scale, including the initial flow rates of the draw and feed solution, operating pressure, membrane permeability-selectivity, structural parameter, and the efficiency of the high-pressure pump (HP), energy recovery device (ERD) and hydro-turbine (HT). The numerical results indicate that the performance of the scaled-up PRO process is significantly dependent on the dimensionless flow rate. Furthermore, with the increase of the specific membrane scale, the accumulated solute leakage becomes important. The membrane to achieve the optimal performance moves to the low permeability in order to mitigate the reverse solute permeation. Additionally, the counter-current flow scheme is capable to increase the process performance with a higher permeable and less selectable membrane compared to the co-current flow scheme. Finally, the inefficiencies of the process components move the optimal APD occurring at a higher dimensionless flow rate to reduce the energy losses in the pressurization and at a higher specific membrane scale to increase energy generation.

No / This paper studies the selective maintenance problem for multi-state series systems working in variable loading conditions in the next mission. In the mission, a component's degradation depends on its current state and the load applied on it. A load-dependent degradation model is proposed for multi-state components operating in variable loading conditions. This model is inspired by the load-sharing model where many components share a common workload and the failure rate of a component depends on the state of other components. A Monte-Carlo simulation method is presented to simulate the multi-state component's degradation and to evaluate the system reliability. The final objective is to determine the best selective maintenance strategy to maximize the expected system reliability in the next mission within available resources. An illustrative example, reliability estimation results, and analysis of optimal selective maintenance scenarios for different levels of budget limitation are provided.

Yes / Thermoacoustic refrigeration is an evolving cooling technology in which the acoustic power is used to pump heat. The operating conditions and geometric parameters are important for the thermoacoustic refrigerator performance, as they affect both its performance and the temperature difference across the stack. This paper investigates the effect of the stack geometric parameters and operating conditions on the performance of a standing wave thermoacoustic refrigerator and the temperature difference across the stack. DeltaEC software is used to make the thermoacoustic refrigerator model. From the obtained results, normalised values for the operating conditions andgeometric parameters are collected to compromise both the performance and the temperature difference across the stack.

La pérennité du compostage est cautionnée à une meilleure maîtrise de la qualité des composts ainsi que des émissions gazeuses. Ces deux critères dépendent en partie des conditions de compostage. Néanmoins, le contrôle des émissions gazeuses et des odeurs ne peut se circonscrire à une seule stratégie préventive. L'identification des composés responsables de l'odeur apparaît comme un enjeu permettant d'augmenter l'efficacité des solutions curatives. Cette thèse a eu pour double objectif d'évaluer l'influence des déterminants des procédés de compostage par aération forcée sur la qualité des composts, les émissions gazeuses et les odeurs en compostage et stockage ainsi que d'investiguer les corrélations entre composition chimique (CC) et concentration d'odeur (CO)des gaz émis. Le déchet et le structurant utilisés ont été des boues d'abattoir de porcs et des plaquettes de bois. L'influence des conditions opératoires a été évaluée en réacteur de compostage au regard des critères que sont la stabilisation, l'hygiènisation, le séchage et la conservation de l'azote. Un taux d'aération intermédiaire, un ratio structurant/déchet (S/D) >1 et une granulométrie >10mm semblent être les conditions répondant au mieux aux attentes d'un traitement par compostage. L'étude de l'influence des conditions sur les composés gazeux suspectés les plus contributrices des odeurs a conduit à une caractérisation exhaustive des émissions d'ammoniac, d'hydrogène sulfuré, des mercaptans par piégeage chimique et des COV par TD-GC-MS. L'identification des composés potentiellement contributeurs de l'odeur s'est basée sur le calcul d'unité d'odeur (UO) de chacun des composés i.e. le rapport de leur concentration chimique sur leur seuil olfactif, à leur pic d'émission. Les conditions opératoires générant le moins d'émissions sont un faible taux d'aération, un ratio S/D >1 et une granulométrie >10mm. Le potentiel des conditions opératoire à diminuer les émissions de composés odorants est toutefois limité. Les travaux venant d'être décrits ont été complétés d'une part par des mesures olfactométriques des émissions et leur corrélation à leur CC et d'autre part par la mise en œuvre de simulations de stockage en vue de comparer leurs émissions et odeurs à celles du compostage. Ces caractérisations ont montré que les pics d'odeurs en compostage sont 50 fois supérieurs à ceux du stockage. Deux types de corrélations entre la CC et la CO des gaz issus du compostage et du stockage ont été investiguées. Le premier type présume que la CO correspond à la somme des UO des composés du mélange. Le second suppose que la CO du mélange corresponde à l'UO la plus élevée (UOMAX) parmi l'ensemble des composés du mélange. Une analyse qualitative et quantitative des types de corrélations testés a indiqué qu'UOMAX est plus proche de la CO mesurée. Seuls trois composés, l'hydrogène sulfuré, le méthanethiol et la triméthylamine rendent compte des odeurs mesurées. / Compost sustainability requires a better control of its compost quality and its gaseous emissions. Both were influenced by composting conditions. Nevertheless, controlling gaseous emissions and their odour can’t be assumed by a single preventive approach. The identification of the compounds involved in odour is also a way to improve the efficiency of curatives solutions. This thesis has been the dual objectives to assess the influence of the principal process conditions for the forced aeration composting on compost quality, gaseous emissions and odours during composting and storage, as well as find out a correlation between the chemical composition (CC) and the odour concentration (OC) of the emissions. The waste and the bulking agent used were pig slaughterhouse sludge and wood chips. The influence of the composting process conditions was studied on stabilization, disinfection, drying and nitrogen conservation during composting in pilot reactors. An intermediate rate, a bulking agent/ waste (BA/W) ratio >1 and a particle size >10mm seemed to be the optimal conditions which satisfy composting treatment expectations. The study of the composting process influence on the gaseous compounds supposed as main potential odour contributors led to an gases exhaustive characterizations, including: ammonia, hydrogen sulphide and mercaptans were quantified by chemical traps while TD-GC-MS was used for VOC. Compounds were screened as main odour contributor based on the compute of their odour unit (OU) of each compounds i.e. dividing their chemical concentration by their odour threshold, at their events of peak emission. The composting process conditions which reduced the emissions were, a low aeration rate, a high BA/W ratio and a particle size > 10mm. Composting process conditions had a limited impact on reducing emissions of odorous compounds. Further works were performed for establish a more accurate odour emission evaluation. On a first hand, olfactometric measurements were carried out in order to be correlated with their CC. On a second hand, experiments were designed to simulate storage with a view to compare their emissions and their odours with that in composting. These characterizations showed that the peaks of the odour emissions were found 50 folds higher during composting than during storage. Two types of correlations were investigated between the CC and the OC of gas samples from composting and storage. The first one assumed that, the OC of gas sample was equal to the sum of the OU of every odorous compound. The second one consisted to consider the OC was equal to the highest OU (OUMAX) of the most odorant compound in the sample. Qualitative and quantitative analyses were tested for the both correlation types, indicating that OUMAX is the expression which can provide the most accurate prediction of OC. Only three main odorous compounds were identified: trimethylamine, hydrogen sulphide and methanethiol.