TY - THES A1 - Dascaliuc, Catalin T1 - A comprehensive study on the gearbox fault diagnosis based on vibration analysis: development of a condition monitoring system N2 - Modern industrial processes run in a continuous fashion, and unexpected shutdowns may lead to significant economic losses. Hence, sustainable maintenance practices are employed in most state-of-the-art sites. Predictive maintenance offers the possibility of mitigating breakdowns and ensures optimum usage of machines. Therefore, the development of reliable condition monitoring systems is crucial in guaranteeing reliable forecasting of machine failures and scheduling of maintenance works. However, since there is active ongoing research in the field of vibration-based condition monitoring, there is an urge to review the latest advances on this topic. Additionally, since condition monitoring is a relatively new branch of engineering, few standards and baselines exist for setting up condition monitoring systems. The current thesis aims to provide a comprehensive framework for gearbox condition monitoring. The aim is to be achieved in the form of developing a baseline GUI for bearing and gear fault analysis. A literature review is conducted to conceptualize gearbox vibration-based fault diagnosis strategies and identify the required signal processing algorithms for fault detection and diagnosis. The software development process is presented alongside the developed MATLAB-based GUI, which is deployed as a standalone application. An assessment of the implemented processing algorithms is done with various numerically generated gearbox signal models. Furthermore, the developed software is used to perform fault diagnosis on experimental data. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-4131 ER - TY - THES A1 - Tahoon, Mohammad T1 - Estimating the State of Charge of Lithium-ion Batteries using Deep Learning for Electric Vehicle Applications N2 - Reliable and safe operation of Li-ion batteries in electric vehicles relies on an accurate estimation of their states, more specifically their state of charge (SOC), which is used for managing charging and discharging, for example. Deep learning is increasingly used for modeling complex systems, which generates more interest in battery state estimation. The theoretical investigation of candidate neural networks for SOC estimation is preceded an experimental approach to evaluate the shortlisted neural networks performance. Publicly available testing datasets representing real driving cycles has been used to train the selected networks, taking into consideration different operating temperatures for the exact cycles. Testing the prediction capability of the models across a realistic range of operating conditions is the driving factor for including testing cycles with a temperature range of [0–40] °C in the training datasets. A demonstration of the full training cycle is conducted, along with the optimization problem of network parameters. This work demonstrates the applicable neural network family used for training this kind of sequential data, followed by the shortlisting of two neural networks based on their performance using literature research. After selecting the networks, finer evaluation criteria is applied, including both their prediction capability and training requirements. The results show that both networks, long short-term memory (LSTM) and gate recurrent units (GRU), offer satisfactory prediction capability with mean absolute error (MAE) of (2.9%, 2.7%) respectively, when averaged across the tested temperatures. The GRU network on the other hand had significantly higher resource requirements and a more complex architecture. Finally, a more recent architecture is briefly discussed, borrowing the strength of a parallel application field. The scope of this work is to highlight both the capabilities and cost of training deep learning networks for SOC estimation. KW - Electric vehicles KW - Lithium-Ion batteries KW - State of charge KW - Deep learning KW - Neural networks Y1 - 2023 ER - TY - THES A1 - Cirilo Levi, Jorge T1 - Simulation of Standard Driving Operations of an Agricultural Tractor with Series-hybrid Powertrain on Asphalts Roads with MATLAB/Simulink N2 - The domain of hybrid electric vehicles (HEV) is not limited to public means of transport, but it is also quite large in the off-road vehicle industry. For this reason, a close inspection of how this type of powertrain works inside an agricultural tractor is indeed quite interesting. A particular agricultural tractor (AT) is chosen for the study of such a system. KW - Modelling and simulation Y1 - 2022 ER - TY - THES A1 - Cheung, Ho Yu T1 - Comparison of wheel and rubber track drives for tractors regarding masses, mass inertias, energy demands and tractor mechanics N2 - Soil compaction is a major concern in agriculture. As farming machinery develops to be larger and heavier, the risk of soil compaction increases. Tractors with rubber tracks have been developed to increase the contact area with soil in order to reduce the risk of soil compaction as well as to increase tractive performance. However, tractors with rubber tracks have increased mass due to the track components. Therefore, the combined effect of rubber tracks was investigated. Comparison of tyres and rubber tracks for tractors on mass, mass inertia, tractor mechanics, energy demand, and soil compaction were investigated. Configurations of tractors consisting of combinations of tyres and interchangeable rubber tracks are evaluated in calculations and in simulation tool Terranimo. Simplified rubber track models were built in order to investigate effect of inertia from the rotational components on energy demand. A theoretical scenario was constructed to calculate the energy demand for the predefined workload. Experimental data in past research showed that the superior tractive performance of rubber track is more apparent in looser soil. In the energy demand calculation part of the thesis the tracked configurations resulted in a higher energy demand. The tracked configurations also showed effect on reducing risk of soil compaction in the same defined scenario. However, the increased energy demand, hence higher fuel consumption and energy efficiency is a downside of the tracked configurations, and the selection between tyre and rubber track should depend on the situation. KW - Rubber tracks KW - Soil compaction KW - Tractor KW - Energy demand KW - Agriculture Y1 - 2022 ER - TY - THES A1 - Hammond, Luciano T1 - Feasibility check, design and dimensioning of a scrounger magnet coupling to a Siemens-E-Highway-truck N2 - Among other factors, electric vehicles (EVs) were developed with the intention of mitigating the transportation sector’s impact on the environment and the depletion of fossil fuels. While various methods exist to charge electric vehicles (EVs) or extend their driving ranges, each have their benefits and drawbacks. As such, a method to accomplish the latter, i.e., to extend the driving range of an EV – and more specifically, a battery electric vehicle (BEV) – is proposed, which entails magnetically coupling to an electric truck (ET) ahead. This is enabled by a front-mounted system, comprised of a magnetic circuit (MC) and a linear axis (LA), which acts as a drawbridge to extend the MC ahead of the vehicle to a steel plate located at the rear of the ET. By utilising an array of permanent magnets (PMs) and a solenoid within a pot ferrite core (PFC) structure, the proposed system is able to easily generate a coupling force of around 6400 𝑁, which is sufficient to maintain the magnetic couple to the ET ahead, even under slight deceleration of the BEV of 2 𝑚 ⁄ s^2, as it recuperates energy via regenerative braking. With the proposed system, a power gain of 30.24 𝑘𝑊 is achievable in this manner, which allows for a significant improvement to the driving range of BEVs, as well as providing similar benefits as those offered by dynamic charging systems, namely, the convenience, and is thus regarded as a feasible solution, with regard to its performance. KW - Master's thesis KW - Magnet coupling Y1 - 2022 ER - TY - THES A1 - Vazquez Hernandez, Marcos T1 - Implementation, execution, and analysis of efficiency measurements to be used in the calculation of CO₂ emissions and fuel consumption of heavy-duty vehicles as per Commission Regulation (EU) 2017/2400 N2 - Quick and reliable transport of goods within the European Union is possible, in part, due to the use of heavy-duty vehicles. Whether long-haul routes, or local and urban deliveries, heavy-duty vehicles are crucial for a large part of EU’s economic activity and the number of these vehicles on the road increases every year. However, they also are main contributors to the total greenhouse gas emissions of the road transport sector and play an important role in the total emissions of the European Union. Therefore, it is of great economic and environmental importance to closely monitor emissions and fuel efficiency of heavy-duty vehicles currently on the road and market, forcing manufacturers to reduce emissions from their vehicles and develop more energy-efficient technologies. For this purpose, the European Commission developed the VECTO simulation tool, reducing costs and time necessary for the approval process and monitoring of new vehicles in the EU. While generic values related to the CO₂ emissions and fuel efficiency characteristics of most components of heavy-duty vehicles are provided by the European Commission to use in VECTO simulations, accurate results require actual component testing. With no VECTO specific testing offered by vehicle testing specialists, this thesis aims to conduct an analysis of Commission Regulation (EU) 2017/2400, guideline for the use of VECTO, to identify requirements, develop and implement testing procedures in experimental tests, and validate results to ensure that the requirements of the regulation are met. KW - Drivetrain testing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-14958 ER -