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This paper explores the noise shaping and noise producing qualities of Delta-Sigma Modulators (DSM) and Pulse-Width Modulators (PWM). DSM has long been dominant in the Delta Sigma Analog-to-Digital Converter (DSADC) as a noise-shaped quantizer and time discretizer, while PWM, with a similar self oscillating structure, has seen use in Class D Power Amplifiers, performing a similar function. It has been shown that the PWM in Class D Amplifiers outperforms the DSM [1], but could this advantage be used in DSADC use-cases? LTSpice simulation and printed circuit board implementation and test are used to present data on four variations of these modulators: The DSM, PWM, the out-of-loop discretized PWM (OOLDP), and the cascaded modulator. A generic form of an Nth order loop filter is presented, where three orders of this generic topology are analyzed in simulation for each modulator, and two orders are used in physical testing.

The objective of this thesis is to develop vibration-based fault detection strategies for on-line condition monitoring of gear transmission systems. The study divides the thesis into three sections. First of all, the local stresses created by a root fatigue crack on a pinion spur gear are analyzed using a quasi-static finite element model and non-linear contact mechanics simulation. Backlash between gear teeth which is essential to provide better lubrication on tooth surfaces and to eliminate interference is included as a defect and a necessary part of transmission design. The second section is dedicated to fixed axis power trains. Torsional vibration is shown to cause teeth separation and double-sided impacts in unloaded and lightly loaded gearing drives. The transient and steady-state dynamic loading on teeth within a two stage crank-slider mechanism arising from backlash and geometric manufacturing errors is investigated by utilizing a non-linear multi-body dynamics software model. The multi-body model drastically reduces the computation time required by finite element methods to simulate realistic operation. The gears are considered rigid with elastic contact surfaces defined by a penalty based non-linear contact formulation. The third section examines a practical differential planetary transmission which combines two inputs and one output. Planetary gears with only backlash errors are compared to those containing both backlash and tooth defects under different kinematic and loading conditions. Fast Fourier Transform (FFT) analysis shows the appearance of side band modulations and harmonics of the gear mesh frequency. A joint time-frequency analysis (JTFA) during start-up reveals the unique vibration patterns for fixed axis gear train and differential planetary gear, respectively, when the contact forces increase during acceleration.

The launch environment experiences many vibration, shock, and acoustic loads. A great concern is the high random vibration levels that can damage components and spacecraft structures, which can cause a mission failure. An effective method of reducing high frequency energy is using isolators. Overall, the need for vibration isolation has been increasing because there has been an increase in the use of mechanisms in which vibrations are prevalent. In addition, there has not been extensive research on the effects of isolators that are outgassed and cured. Therefore, it is important to investigate and understand the vibrational effects on isolators. It is convenient to outgas and cure isolators for the benefit of some components because that eliminates contamination, but outgassing and bake-out could potentially affect the frequency response of the isolator system in a negative way. It is valuable to investigate how much outgassing and bake-out might affect the performance, if any, of the vibration isolator in order to benefit companies who may need that kind of information. The vibration isolators used for the purpose of this research were twenty E22-02-40 isolators provided by Barry Controls, a Hutchinson Group Company. The E22-02-40 isolators were outgassed and cured (heated at high temperatures) in order to be turned from grade C into grade A level for outgassing. Then the vibrational performance of those isolators were tested and compared to the isolators that were not outgassed and not cured. Vibration tests were run in the flat frequency spectrum and high frequency spectrum. The maximum percent difference occurred in the grade A level isolators, in which the first frequency mode increased by 33.3 % in the z direction from the grade C isolators. A numerical finite element analysis was performed on Abaqus/CAE in order to verify the experimental results. In addition, a swelling test was conducted on the isolators to test their physical characteristics change after they were outgassed and cured.

This thesis focuses on the simulation of the gyroscopic effect using the software MSC Adams. A simple shaft-disk system was created and parameter of the sys-tem were changed in order to study the influence of the gyroscopic effect. It was shown that an increasing bearing stiffness reduces the precession motion. Fur-thermore, it was shown that the gyroscopic effect vanishes if the disk of system is placed symmetrically on the shaft, which reduces the system to a Jeffcott-Ro-tor. The second objective of this study was to analyze different defects in a simple fixed axis gear set. In particular, a cracked shaft, a cracked pinion and a chipped pinion as well as a healthy gear system were created and tested in Adams. The contact force between the two gears was monitored and the 2D and 3D frequency spectrum, as well as the Wavelet Transform, were plotted in order to compare the individual defects. It was shown that the Wavelet Transform is a powerful tool, capable of identifying a cracked gear with a non-constant speed. The last part of this study included fault detection with statistical methods as well as with the Sideband Energy Ratio (SER). The time domain signal of the individual faults were used to compare the mean, the standard deviation and the root mean square. Furthermore, the noise profile in the frequency spectrum was tracked with statistical methods using the mean and the standard deviation. It was demonstrated that it is possible to identify a cracked gear, as well as a chipped gear, with statistical methods. However, a cracked shaft could not be identified. The results also show that SER was only capable to identify major defects in a gear system such as a chipped tooth.

This study presents a procedure for the estimation of stopping behavior of industrial robots with a trained neural network. This trained network is presented as a single channel in a redundant architecture for safety control applications, where its potential for future integration with an analytical model of robot stopping is discussed. Basic physical relations for simplified articulated manipulators are derived, which motivate a choice of quantities to predict robot stopping behavior and inform the training and testing of a network for prediction of stopping distances and times. Robot stopping behavior is considered in the context of relevant standards ISO 10218-1, ISO/TS 15066 and IS0 13849-1, which inform the definitions for safety related stopping distances and times used in this study. Prior work on the estimation of robot stopping behavior is discussed alongside applications of machine learning to the broader field of industrial robotics, and particularly to the cases of prediction of forward and inverse kinematics with trained networks. A state-driven data collection program is developed to perform repeated stopping experiments for a controlled stop on path within a specified sampling domain. This program is used to collect data for a simulated and real robot system. Special attention is given to the identification of meaningful stopping times, which includes the separation of stopping into pre-deceleration and post-deceleration phases. A definition is provided for stopping of a robot in a safety context, based on the observation that residual motion over short distances (less than 1 mm) and at very low velocities (less than 1 mm/s) is not relevant to robot safety. A network architecture and hyperparameters are developed for the prediction of stopping distances and times for the first three joints of the manipulator without the inclusion of payloads. The result is a dual-network structure, where stopping distance predictions from the distance prediction network serve as inputs to the stopping time prediction network. The networks are validated on their capacity to interpolate and extrapolate predictions of robot stopping behavior in the presence of initial conditions not included in the training and testing data. A method is devised for the calculation of prediction errors for training training, testing and validation data. This method is applied both to interpolation and extrapolation to new initial velocity and positional conditions of the manipulator. In prediction of stopping distances and times, the network is highly successful at interpolation, resulting in comparable or nominally higher errors for the validation data set when compared to the errors for training and testing data. In extrapolation to new initial velocity and positional conditions, notably higher errors in the validation data predictions are observed for the networks considered. Future work in the areas of predictions of stopping behavior with payloads and tooling, further applications to collaborative robotics, analytical models of stopping behavior, inclusion of additional stopping functions, use of explainable AI methods and physics-informed networks are discussed. / Master of Science / As the uses for industrial robots continue to grow and expand, so do the need for robust safety measures to avoid, control, or limit the risks posed to human operators and collaborators. This is exemplified by Isaac Asimov's famous first law of robotics - "A robot may not injure a human being, or, through inaction, allow a human being to come to harm." As applications for industrial robots continue to expand, it is beneficial for robots and human operators to collaborate in work environments without fences. In order to ethically implement such increasingly complex and collaborative industrial robotic systems, the ability to limit robot motion with safety functions in a predictable and reliable way (as outlined by international standards) is paramount. In the event of either a technical failure (due to malfunction of sensors or mechanical hardware) or change in environmental conditions, it is important to be able to stop an industrial robot from any position in a safe and controlled manner. This requires real-time knowledge of the stopping distance and time for the manipulator. To understand stopping distances and times reliability, multiple independent methods can be used and compared to predict stopping behavior. The use of machine learning methods is of particular interest in this context due to their speed of processing and the potential for basis on real recorded data. In this study, we will attempt to evaluate the efficacy of machine learning algorithms to predict stopping behavior and assess their potential for implementation alongside analytical models. A reliable, multi-method approach for estimating stopping distances and times could also enable further methods for safety in collaborative robotics such as Speed and Separation Monitoring (SSM), which monitors both human and robot positions to ensure that a safe stop is always possible. A program for testing and recording the stopping distances and times for the robot is developed. As stopping behavior varies based on the positions and speeds of the robot at the time of stopping, a variety of these criteria are tested with the robot stopping program. This data is then used to train an artificial neural network, a machine learning method that mimics the structure of human and animal brains to learn relationships between data inputs and outputs. This network is used to predict both the stopping distance and time of the robot. The network is shown to produce reasonable predictions, especially for positions and speeds that are intermediate to those used to train the network. Future improvements are suggested and a method is suggested for use of stopping distance and time quantities in robot safety applications.

Microprocessor-controlled knees (MPK’s) allow amputees to walk with increasing ease and safety as technology progresses. As an amputee is fitted with a new MPK, the knee’s internal parameters are tuned to the user’s preferred settings in a controlled environment. These parameters determine various gait control settings, such as flexion target angle or swing extension resistance. Though these parameters may work well during the initial fitting, the MPK experiences various internal & external environmental changes throughout its life-cycle, such as product wear, changes in the amputee’s muscle strength, temperature changes, etc. This work investigates the feasibility of using a reinforcement learning (RL) control to adapt the MPK’s swing resistance to consistently induce the amputee’s preferred swing performance in realtime. Three gait features were identified as swing performance indicators for the RL algorithm. Results show that the RL control is able to learn and improve its tuning performance in terms of Mean Absolute Error over two 40-45 minute training sessions with a human-in-the-loop. Additionally, results show promise in using transfer learning to reduce strenuous RL training times. / Mikroprocessorkontrollerade knän (MPK) gör att amputerade kan utföra fysiska aktiviteter med ökad lätthet och säkerhet allt eftersom tekniken fortskrider. När en ny MPK monteras på en amputerad person, anpassas knäts interna parametrar till användarens i ett kontrollerad miljö. Dessa parametrar styr olika gångkontrollinställningar, såsom flexionsmålvinkel eller svängförlängningsmotstånd. Även om parametrarna kan fungera bra under den initiala anpassningen, upplever den MPK olika interna och yttre miljöförändringar under sin hela livscykel, till exempel produktslitage, förändringar i den amputerades muskelstyrka, temperaturförändringar, etc. Detta arbete undersöker möjligheten av, med hjälp av en förstärkningsinlärningskontroll (RL), att anpassa MPK svängmotstånd för att konsekvent inducera den amputerades föredragna svängprestanda i realtid. Tre gångegenskaper identifierades som svingprestandaindikatorer för RL-algoritmen. Resultaten visar att RL-kontrollen kan lära sig och förbättra sin inställningsprestanda i termer av Mean Absolute Error under två 40-45 minuters träningspass med en människa-i-loopen. Dessutom är resultaten lovande när det gäller att använda överföringsinlärning för att minska ansträngande RL-träningstider.

Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced the performance, vehicle range, energy consumption, regenerated energy using specific EDUs provided by sponsors. Adhering to a systematic engineering iterative method, the emphasis was heavily placed on simulation and modeling during the development and validation of these strategies. Simulations ensured robust testing before field implementation, emphasizing software modeling's vital role.

This paper describes the design, manufacturing, and testing of a novel controllable hypoxic incubator with fully functional oxygen gas control and temperature control in a humid environment. On the current market, a majority of the few hypoxic incubators use pre-mixed gas that does not offer precise control over gas concentration. The objective for this project was to create a chamber that allows the user to set the O2 concentration to varying set points of % O2 while maintaining the chamber at a constant body temperature, CO2 level, humidity, and sterility. To start the project, multiple concepts were developed for the chamber design and the control system. These concepts were compared against developed engineering specs and were evaluated amongst the team and sponsor. From there, a detailed CAD model was developed and utilized to design the structure and was used as a guide for manufacturing. The control system was prototyped on breadboards via Arduino. This breadboard testing served as the map to solder perf boards, which are utilized as the final structure for the control system. Once all parts were sourced, machined, and assembled for the final chamber and the control system, these subassemblies were integrated together with a regulated gas system via various tubing. The integrated final design underwent a variety of testing to validate the incubator design and control system. Testing was performed throughout the course of this project: material testing, gas leak testing, cell test, temperature control test, and gas control system optimization; however, the most important of these tests were those relating to the environmental control of the incubator. These tests confirmed whether the incubator design was functional as a practical incubator. Testing confirmed that O2 and temperature control maintained in spec over a short and long period of time while maintaining a humid environment. CO2 control optimization had more complications than the O2 hypoxia system. During testing CO2 concentration would typically overshoot the set point, likely due to a lack of precise control over the gas flow. CO2 variability was reduced due to optimization in the code, but not fully mitigated. Future iterations of this chamber could improve upon the CO2 control and streamline the user interface.

IT-utvecklingen har förändrat företags sätt att bedriva verksamheter och den snabba förändringen som följer med IT-utvecklingen har haft en betydande inverkan på revisionsbranschen. Enligt ISA 315 behöver revisorer ha en bred förståelse för företagets IT-miljö för att kunna genomföra en noggrann revision. Samtidigt finns det en potential för revisorn att använda automatiserade verktyg och tekniker för att förbättra både kvaliteten och effektiviteten i revisionsprocessen. Det är därför avgörande att revisorer inte bara utökar sina IT-kompetenser utan också använder dessa automatiserade verktyg och tekniker på ett effektivt sätt för att möta de utmaningar och krav som följer med den moderna revisionen. Syfte: Med anledning av de nya kraven som ställs på revisorn i ISA 315 syftar vår studie till att ge oss en förståelse om vilka utmaningar och möjligheter revisorer möter under revisionsprocessen när det gäller identifiering och bedömning av IT-relaterade risker av det reviderade företaget. Metod: För att uppfylla studiens syfte genomfördes en kvalitativ forskningsmetod, en kombination av kvalitativa intervjuer och en dokumentstudie. Genom att använda dessa metoder samlades data in och insikter erhölls om de utmaningar och möjligheter som revisorer står inför vid identifiering och bedömning av IT-relaterade risker under revisionsprocessen. Resultat: Studiens slutsatser indikerar att revisorer står inför utmaningar när det gäller att förstå och anpassa sig till kraven i ISA 315, vilket kräver en djup förståelse av företagets IT-miljö och investering av tid och resurser för att utföra en grundlig revision. Revisorer behöver öka sin IT-kompetens och uppdatera sina kunskaper för att kunna utföra avancerade analyser med hjälp av automatiserade verktyg och tekniker. Vidare understryks betydelsen av professionell skepticism och behovet av att kombinera IT-kompetens med en djupare förståelse av företagets IT-miljö för att utföra en effektiv revision. Slutligen identifieras användningen av automatiserade verktyg och tekniker som en möjlighet för revisorer att hantera utmaningar i den snabbt föränderliga teknologiska miljön, vilket kan förbättra revisionens kvalitet och effektivitet. / IT development has transformed how companies conduct their operations, and the rapid changes accompanying IT development have had a significant impact on the auditing industry. According to ISA 315, auditors need to have a comprehensive understanding of a company's IT environment to perform a meticulous audit. Simultaneously, there is potential for auditors to leverage automated tools and techniques to enhance both the quality and efficiency of the audit process. It is therefore crucial for auditors not only to expand their IT competencies but also to utilize these automated tools and techniques effectively in order to address the challenges and requirements associated with modern auditing. Purpose: With reference to the new requirements imposed on auditors in ISA 315, our study aims to provide us with an understanding of the challenges and opportunities auditors encounter during the audit process regarding the identification and assessment of IT-related risks of the audited company. Method: To fulfill the purpose of the study, a qualitative research methodology was employed, using a combination of qualitative interviews and document analysis. These methods were used to collect data and gain insights into the challenges and opportunities faced by auditors in identifying and assessing IT-related risks during the audit process. Results: The study's findings indicate that auditors face challenges in understanding and adapting to the requirements of ISA 315, which necessitates a deep understanding of the company's IT environment and investment of time and resources to perform a thorough audit. Auditors need to enhance their IT competence and update their knowledge to conduct advanced analyses using automated tools and techniques. Furthermore, the importance of professional skepticism is emphasized, along with the need to combine IT competence with a deeper understanding of the company's IT environment to carry out an effective audit. Lastly, the use of automated tools and techniques is identified as an opportunity for auditors to address challenges in the rapidly changing technological landscape, enhancing the quality and efficiency of the audit process.