M. Tech. (Department of Metallurgical Engineering, Faculty of Engineering and Technology), Vaal University of Technology. / To meet the ever-increasing demand for maintenance of gear systems, industrial companies have traditionally depended on the shutdown of the machines before processing the fault diagnosis. Nowadays, online monitoring has proven to be effective in terms of machine state analysis and fault prediction. However, the application of such a technique in the analysis of combined multiple nonlinear faults is still a subject of research. The vibration signature of a coexisting nonlinear crack and pit in two-stage gear system is unknown, it can be regarded as one of the most difficult problems to extract and diagnose. Additionally, incorporating both a crack and a pit into numerical models is a time-consuming process that demands a breadth of mechanical understanding. Diagnostics of faulty gears, on the other hand, can be performed in the time and frequency domain or in the Time-Frequency domain, depending on the complexity of the vibration. Non-linear and non-stationary phenomena (Features) occur when repeated pitting and cracking faults occur, reducing the reliability of standard signal processing methods (Gear displacement and Fast Fourier Transform). This thesis solves each of these shortcomings by developing an eight-degree-of-freedom (DOF) gear model with a breathing crack and multiple pitted gear teeth. The identified spur-gear model enabled the investigation of the crack and pitting signatures and their effect on the ensuing vibrations independently of the action of other system components. Additionally, when pitting and cracking coexist, the study was conducted to determine the influence of such a failure on the transmission system. Theoretical results indicated that the presence of pitting and crack in the tooth of the gear resulted in a decrease in mesh stiffness. Additionally, the influence of the breathing pitting and crack results in material fatigue, which results in the generation of a random term in the vibration signal. To corroborate the acquired results, several experimental tests on a spur-gear test rig with a defined pit and crack size range were undertaken under a variety of conditions. In comparison to the presented methodologies, theoretical and experimental results indicate that 3D Frequency-RPM analysis is the most sensitive and resilient method for the early detection and identification of pit and crack faults. Furthermore, when crack or pit faults are studied individually, the STFT analysis yields interesting results. The feature analysis revealed that, when using the Time-Frequency technique, the crack and pit combination in a two-stage gear system is a priori more efficient than the other options.

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The objective of this dissertation is to propose circuit architectures and techniques for built-in test and calibration of phase-locked loops. The design of phase-locked loops is first investigated to achieve a robust performance over process, temperature, voltage corners with minimum overhead. Different design techniques including adding loop programmability, increasing area efficiency, reducing noise immunity, and increasing frequency coverage are discussed. Secondly, built-in testing of phase-lock loops using sensors are proposed for loop dynamic parameters and reference spur. An integrator is designed to extract the subtle response from the system so that target parameters can be predicted. Different testing methodologies are applied different specification testing as well. Finally, an on chip phase-locked loop design is implemented for reference spur calibration. The phase-locked loop is designed with a programmable reference spur range. A static phase offset detector is included to identify the optimal setting of reference spur in the feedback system. The integrated jitter performance is improved by the calibration mechanism. The results of this thesis serve as an on-chip built-in self-test and self-calibration solution for embedded phase-locked loops in a high integration system.

Orientador: Aparecido Carlos Gonçalves / Banca: Gilberto Pechoto de Melo / Banca: Jorge Nei Brito / Resumo: Todo sistema ou equipamento mecânico está sujeito a processos de deterioração. Esta deterioração leva ao aparecimento de defeitos que podem atrapalhar a continuidade e qualidade do serviço (produção). Uma quebra não prevista traduz-se por uma parada brusca, levando geralmente a grandes prejuízos e a perda de tempo de produção. Para evitar paradas inesperadas eficientemente e reduzir os custo de manutenção são utilizadas técnicas de manutenção preditiva. Entre todas as técnicas existentes, as mais importantes na manutenção de redutores são a análise de vibrações e a análise de partículas de desgaste. A determinação da severidade, modos e tipos de desgaste em máquinas pode ser feita pela identificação da morfologia, acabamento superficial, coloração, natureza, quantidade e tamanho das partículas encontradas em amostras de óleo ou graxas. Pela análise do sinal vibratório é possível tomar decisões de intervir ou não na operação da máquina, aumentando a disponibilidade e confiabilidade da mesma. A integração destas técnicas tem o potencial para revolucionar práticas industriais e proporcionar para as companhias um significante alívio econômico. Para estudo destas duas técnicas, neste trabalho foi construída uma bancada experimental com o propósito de se realizar a manutenção preditiva em um redutor de velocidades. O Sistema foi posto para funcionar até o limite de utilização. Foram utilizados lubrificantes recomendados pelo fabricante do redutor, lubrificantes com contaminação líquida em várias porcentagens e lubrificantes com contaminação sólida em duas porcentagens. Foram observadas que para baixas rotações a análise de partículas foi vantajosa em relação à análise de vibrações e que a contaminação por água, embora tenha ocasionado uma maior oxidação do sistema, proporcionou menor desgaste do que o lubrificante sem contaminação. / Abstract: Every system or mechanical equipment is subject to deterioration processes. This deterioration takes to the apparition of defects that can disturb the continuity and quality of the service (production). One break not foreseen translates for an abrupt stop, usually taking to great damages and waste of time of production. For avoid unexpected stops efficiently and reduce the cost, predictive maintenance techniques are used. Among all the existent techniques, the most important in the maintenance of reducers are vibration analysis and wear particle analysis. The determination of the severity, wear modes and types in machines can be done through the identification of the morphology, surface finishing, coloration, nature, quantity and size of the particles found in oil samples or lubricating greases. Vibration Analysis is one of the oldest techniques in the maintenance. By vibration sign analysis it is possible to make decisions of intervening or not in the machine operation, improving its availability and reliability. The integration of these techniques has the potential to revolutionize industry practices and provide a significant industrial economic relief. For the study of these two techniques, in this work, an experimental bench was built with the purpose of doing the predictive maintenance in a reducer. The System was put to work until the limit. It was observed that for low rotations the wear particle analysis were advantageous in relation to the vibration analysis and for oil with water contamination, although causing a larger oxidation in the system, a smaller wear than the lubricant without contamination was provided. / Mestre

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The aim of this master's thesis is to build artificial neural network that is able to calculate varying single tooth-pair mesh stiffness of spur gear for given input parameters. The training set for this network was determined by computational modelling by finite element method. Therefore, creating of computational model and mesh stiffness calculating were a partial aim of this thesis. Input parameters for stiffness calculation were number of driving and driven gear teeth and gear loading. Creating of computational model and performing series of simulations was followed by creating artificial neural network. Multilayer neural network with backpropagation training was chosen as a type of the network. Created neural network is sufficiently efficient and can determine varying mesh stiffness in input set range for learned input parameters and for values of parameters that are not included in training set as well. This neural network can be used for varying single tooth-pair mesh stiffness estimation in input set range.

The diploma thesis deals with the perpendicular crossing of tram track with railway siding. The structure of crossing is described both in the Czech Republic and in the world. The aim of the thesis is to find the recommended design solution for the crossing of tram and railway tracks with regard to legislation and feasibility. After a comparison of the existing structures is chosen which satisfies the conditions of feasibility.

This diploma thesis is focused on the implementation of the educational role of the experimental station for measuring the effectiveness of gears. The proposed laboratory task "Experimental measurement of the effectiveness of gears" is intended to familiarize students with the basic principles of gears and especially highlight the issue of the effectiveness of gears. In this presented thesis, it is also suggested the possibility of expanding the educational use of the experimental station. This extension deals with vibration diagnostics of frequencies of gears.

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No description available.