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This thesis treats the design and implementation of a power distribution network for a controller PCB for controlling charging of electric vehicles. The controller PCB is powered by mains power, and thus needs both AC to DC conversion and DC to DC conversion in order to operate. The thesis focuses on the design of an isolated flyback topology AC to DC converter, while also describing the design and implementation of the DC to DC converters needed for the controller PCB to operate. The work started with some theoretical study, and then progressed into designing the converters. The AC to DC and the DC to DC converters where designed in parallel. After the design phase was complete the converters where implemented on PCBs for evaluation. The evaluation of the AC to DC converter involved evaluation of several different transformers from different suppliers, as well as evaluation of the circuit design itself. All converters designed proved functional after evaluation.

Improving the reliability and efficiency of rotating machinery are central problems in many application domains, such as energy production and transportation. This requires efficient condition monitoring methods, including analytics needed to predict and detect faults and manage the high volume and velocity of data. Rolling element bearings are essential components of rotating machines, which are particularly important to monitor due to the high requirements on the operational conditions. Bearings are also located near the rotating parts of the machines and thereby the signal sources that characterize faults and abnormal operational conditions. Thus, bearings with embedded sensing, analysis and communication capabilities are developed.   However, the analysis of signals from bearings and the surrounding components is a challenging problem due to the high variability and complexity of the systems. For example, machines evolve over time due to wear and maintenance, and the operational conditions typically also vary over time. Furthermore, the variety of fault signatures and failure mechanisms makes it difficult to derive generally useful and accurate models, which enable early detection of faults at reasonable cost. Therefore, investigations of machine learning methods that avoid some of these difficulties by automated on-line adaptation of the signal model are motivated. In particular, can unsupervised feature learning methods be used to automatically derive useful information about the state and operational conditions of a rotating machine? What additional methods are needed to recognize normal operational conditions and detect abnormal conditions, for example in terms of learned features or changes of model parameters?   Condition monitoring systems are typically based on condition indicators that are pre-defined by experts, such as the amplitudes in certain frequency bands of a vibration signal, or the temperature of a bearing. Condition indicators are used to define alarms in terms of thresholds; when the indicator is above (or below) the threshold, an alarm indicating a fault condition is generated, without further information about the root cause of the fault. Similarly, machine learning methods and labeled datasets are used to train classifiers that can be used for the detection of faults. The accuracy and reliability of such condition monitoring methods depends on the type of condition indicators used and the data considered when determining the model parameters. Hence, this approach can be challenging to apply in the field where machines and sensor systems are different and change over time, and parameters have different meaning depending on the conditions. Adaptation of the model parameters to each condition monitoring application and operational condition is also difficult due to the need for labeled training data representing all relevant conditions, and the high cost of manual configuration. Therefore, neither of these solutions is viable in general.   In this thesis I investigate unsupervised methods for feature learning and anomaly detection, which can operate online without pre-training with labeled datasets. Concepts and methods for validation of normal operational conditions and detection of abnormal operational conditions based on automatically learned features are proposed and studied. In particular, dictionary learning is applied to vibration and acoustic emission signals obtained from laboratory experiments and condition monitoring systems. The methodology is based on the assumption that signals can be described as a linear superposition of noise and learned atomic waveforms of arbitrary shape, amplitude and position. Greedy sparse coding algorithms and probabilistic gradient methods are used to learn dictionaries of atomic waveforms enabling sparse representation of the vibration and acoustic emission signals. As a result, the model can adapt automatically to different machine configurations, and environmental and operational conditions with a minimum of initial configuration. In addition, sparse coding results in reduced data rates that can simplify the processing and communication of information in resource-constrained systems.   Measures that can be used to detect anomalies in a rotating machine are introduced and studied, like the dictionary distance between an online propagated dictionary and a set of dictionaries learned when the machine is known to operate in healthy conditions. In addition, the possibility to generalize a dictionary learned from the vibration signal in one machine to another similar machine is studied in the case of wind turbines.   The main contributions of this thesis are the extension of unsupervised dictionary learning to condition monitoring for anomaly detection purposes, and the related case studies demonstrating that the learned features can be used to obtain information about the condition. The cases studies include vibration signals from controlled ball bearing experiments and wind turbines; and acoustic emission signals from controlled tensile strength tests and bearing contamination experiments. It is found that the dictionary distance between an online propagated dictionary and a baseline dictionary trained in healthy conditions can increase up to three times when a fault appears, without reference to kinematic information like defect frequencies. Furthermore, it is found that in the presence of a bearing defect, impulse-like waveforms with center frequencies that are about two times higher than in the healthy condition are learned. In the case of acoustic emission analysis, it is shown that the representations of signals of different strain stages of stainless steel appear as distinct clusters. Furthermore, the repetition rates of learned acoustic emission waveforms are found to be markedly different for a bearing with and without particles in the lubricant, especially at high rotational speed above 1000 rpm, where particle contaminants are difficult to detect using conventional methods. Different hyperparameters are investigated and it is found that the model is useful for anomaly detection with as little as 2.5 % preserved coefficients.

This thesis presents a novel system-level synthesis framework called System-Level Architectural Synthesis Framework (SYLVA), which synthesizes DigitalSignal Processing (DSP) sub-systems modeled by synchronous data ?ow intohardware implementations in Application-Specific Integrated Circuit (ASIC),Field-Programmable Gate Array (FPGA) or Coarse-Grained ReconfigurableArchitecture (CGRA) style. SYLVA synthesizes in terms of pre-characterizedFunction Implementations (FIMPs). It explores the design space in threedimensions, number of FIMPs, type of FIMPs, and pipeline parallelism be-tween the producing and consuming FIMPs. SYLVA also introduces timingand interface model of FIMPs to enable reuse and automatic generation ofGlobal Interconnect and Control (GLIC) to glue the FIMPs together into aworking system. SYLVA has been evaluated by applying it to several realand synthetic DSP applications and the experimental results are analyzedfor the design space exploration, the GLIC synthesis, the code generation,and the CGRA floorplanning features. The conclusion from the experimentalresults is that by exploring the multi-dimensional design space in terms ofpre-characterized FIMPs, SYLVA explores a richer design space and does itmore effectively compared to the existing High-Level Synthesis (HLS) toolsto improve both engineering and computational efficiency. / <p>QC 20160125</p>

A theoretical investigation of existing sensor techniques, bothcommercial sensors and scientific studies, has been performed inorder to find a cost efficient fluid sensor with the ability todetect small amounts of non-conducting fluids. From these studies,six different techniques could be distinguished. The techniques weretested and compared, both in theory and practically, against certaincriteria’s such as temperature and movement sensibility. Three of thetechniques have been proved to work and two of them were built,installed and tested on an industrial robot manufactured by ABBRobotics. The two most promising techniques distinguished were a photointerrupter and a Quartz Crystal Microbalance sensor. After tests itcould be concluded that both sensors fulfilled all preferences. However out of the two, the Quartz Crystal Microbalance sensorperformed best and could detect smaller amounts of fluid more quicklyand reliably than the photo interrupter. This work has resulted in a patent application.

The electrification of the automotive industry is growing in popularity, considering the environmental impacts of the conventional diesel-powered automobile. However, from the electromagnetic compatibility (EMC) viewpoint, it is observed that the use of variable-frequency drives (VFD) and relatively high-power cables to propel electrical motors has led to a considerable rise in electromagnetic interference (EMI) within and outside the machine. EMI could come from the fast switching of the inverter, electromagnetic radiation from the high- power cables, common mode and differential mode currents as well as parasitic coupling of some of the components in the machine. The signals transmitted by near-by communication cables can be distorted as a result or, in the worst case, interference with the controller area network (CAN) bus of the machine.This thesis work aims to investigate different ways of mitigating EMI in battery-electric mine trucks used for underground mining. Having a three-phase system with power cables consisting of three conductors per phase per traction motor connecting the variable frequency drives (VFD) to the motors, the electromagnetic emission is significantly high because of the current level transmitted by the cables. This is in addition to the fast switching frequency of the inverter as the load/torque varies. Cable models are made using a finite element method (FEM) simulation tool, Ansys electronics desktop. The models are used to study how the cable shielding and material, arrangements and phase orientation can impact the radiated EMI within the machine. Experimental measurements are made in order to validate the models. Parasitic coupling between cables and components such as shield and protective earth conductors is considered to estimate the emitted magnetic fields. Results from one of the simulations show that a hybrid shield consisting of 50% Mu metal and 50% copper will give shield effectiveness up to 65% with reference to when an only copper shield is used. Mu-metal is the next most recommended shield because of the system low fundamental frequency. Steel shield gives as high as 20% better shielding than copper.Further simulations present the trefoil placement of the cable bundles, with the center bundle positioned upside-down compared to the two outer bundles, as a better option compared to when the cables within bundles are placed in a linear configuration, although the difference in the induces EMI is only approximately 5%.The major conditions for the above stated preferred arrangements include that bundles of cables within each bundle are tightly held together and the phase orientation is such that a cable is placed farthest away from the cable with the corresponding phase in the neighbouring bundle. Study on the effect of the connection of cable shield shows that common mode current is increased with the shield connected to ground through the body of the machine. This will give a considerable rise to both conducted and radiated EMI, but could help to reduce the risk of current flowing in uncontrolled parts of the machine.

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The project deals with both experimental and simulation analysis of the synchronous machine. The effect of Eddy currents on the impedance is verified experimentally. A core set-up is designed and analysed for the influence of Eddy current in the system. The set-up is altered to reduce the effect of Eddy current and to validate the effect of it on the magnetic inductance.

Manufacturing production systems are increasingly forced to join the path of sustainability regarding their typical room for improvement in terms of clean technology and energy usage. However, implementing these eco-friendly measures on facilities is a double-edged sword since the results are not usually guaranteed and could end up being extra energy wastages. Even though nowadays it is usually made for sequence fixing and training tasks, virtual commissioning comes alternatively into action showing up to test energy optimization attempts preventing the premature execution issues that could happen. This project has developed a VC model of a gantry crane system from a Volvo operation including energy consumption monitoring, aiming to test its suitability on energy optimization tasks. The development has been accomplished following the design and creation methodology through the use of Matlab, Codesys and Simumatik 3D and quantitative results are given specifically from different energy modeling drafts until reaching the closest result to the real system consumption. Once the true to reality model was developed, the optimization test was carried out decreasing the maximum velocity of the system behavior to see the energy consumption variation. This constitutes the ultimate test and its results are discussed coming into the conclusion that the VC model is suitable for energy optimization of the treated operation but would require reconfigurations for aggressive velocity changes.

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