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.

Smart structure control systems commonly use piezoceramic sensors or accelerometers as vibration measurement devices. These measurement devices often produce noisy and/or low-precision signals, which makes it difficult to measure small-amplitude vibrations. Laser interferometry devices pose as an alternative high-precision position measurement method, capable of nanometer-scale resolution. The aim of this research is to utilize a model-based design approach to develop and implement a real-time Linear Quadratic Gaussian (LQG) regulator for a piezoelectric actuated smart structure using a high-precision laser interferometry measurement system to suppress the excitation of vibratory modes. The analytical model of the smart structure is derived using the extended Hamilton Principle and Euler-Bernoulli beam theory, and the equations of motion for the system are constructed using the assumed-modes method. The analytical model is organized in state-space form, in which the effects of a low-pass filter and sampling of the digital control system are also accounted for. The analytical model is subsequently validated against a finite-element model in Abaqus, a lumped parameter model in Simscape Multibody, and experimental modal analysis using the physical system. A discrete-time proportional-derivative (PD) controller is designed in a heuristic fashion to serve as a baseline performance criterion for the LQG regulator. The Kalman Filter observer and Linear Quadratic Regulator (LQR) components of the LQG regulator are also derived from the state-space model. It is found that the behavior of the analytical model closely matches that of the physical system, and the performance of the LQG regulator exceeds that of the PD controller. The LQG regulator demonstrated quality estimation of the state variables of the system and further constitutes an exceptional closed-loop control system for active vibration control and disturbance rejection of the smart structure.

Denna rapport sammanfattar resultat av ett examensarbete på en högskoleingenjörsutbildningsom utfördes av två studenter på Malmö högskola.Arbetets syfte var att genomföra en mängd olika experiment med accelerometer, barometeroch GPS i en modern smarttelefon. Ett antal scenarier för en tänkbar sensoranvändning i applikationerformulerades för att vägleda olika experiment. Experimentdata dokumenterades noggrantoch analyserades med avsikten att skapa en databank med information för framtida studier.Analys av data inkluderar höjdbestämning i naturen och i byggnader med hjälp av barometersensor,geografisk position med hjälp av GPS, hastighet och acceleration under en hissfärdmed hjälp av accelerometer.Rapporten innehåller en omfattande litteraturstudie om användning av sensorer vid inomhuspositionering.Från analys av mätdata, kom vi fram till slutsatsen att är möjligt att beräkna höjdenfrån barometerdata med bra noggrannhet under optimala omständigheter. GPS höjdenfrån mätningarna har stor felmarginal jämfört med den verkliga höjden samt när den jämförsmed den beräknade höjden från barometern.Genom att utföra en numerisk integration på accelerometer-data kom vi fram till att det är möjligtatt beräkna ungefär hur långt man har färdats med en hiss, dock att vissa detaljer måste tasi beaktning. / This report summarizes the results of a degree Bachelor of engineering in Computer Scienceconducted by two students at Malmo University.Work aim was to conduct a variety of experiments with accelerometer, barometer and GPS in amodern smartphone. A number of scenarios for a possible sensor use in applications formulatedto guided experiments. Data is carefully documented and analyzed, with the intention tocreate a database of information for future studies. Analysis of the data includes the altitudedetermination in nature and in buildings using barometric sensor, geographic location usingGPS, speed and acceleration during an elevator journey with the help of accelerometer.The report contains a comprehensive literature review on the use of sensors for indoorpositioning.From our analysis of the measurement data, we conclude that it is possible to calculate thealtitude from barometric- information but good accuracy if there are optimum circumstances.GPS altitude from our measurements show faulty height by a large margin compared with theactual height and when it is compared with the calculated height of the barometer the barometricheight is closer to the actual height.By performing a numerical integration of the accelerometer data, the results show that it ispossible to calculate approximately how far you have traveled in meters in an elevator, howeverthere are some things that must be taken into consideration.

<p>Microbiological contamination from bacteria such as <em>Escherichia coli</em> and Salmonella is one of the main reasons for waterborne illness. Real time and accurate monitoring of water is needed in order to alleviate this human health concern. Performing multiple and parallel analysis of biomarkers such as DNA and mRNA that targets different regions of pathogen functionality provides a complete picture of its presence and viability in the shortest possible time. These biomarkers are present inside the cell and need to be extracted for analysis and detection. Hence, lysis of these pathogenic bacteria is an important part in the sample preparation for rapid detection. In addition, collecting a small amount of bacteria present in a large volume of sample and concentrating them before lysing is important as it facilitates the downstream assay. Various techniques, categorized as mechanical, chemical, thermal and electrical, have been used for lysing cells. In the electrical method, cells are lysed by exposure to an external electric field. The advantage of this method, in contrast to other methods, is that it allows lysis without the introduction of any chemical and biological reagents and permits rapid recovery of intercellular organelles. Despite the advantages, issues such as high voltage requirement, bubble generation and Joule heating are associated with the electrical method.</p> <p>To alleviate the issues associated with electrical lysis, a new design and associated fabrication process for a microfluidic cell lysis device is described in this thesis. The device consists of a nanoporous polycarbonate (PCTE) membrane sandwiched between two PDMS microchannels with electrodes embedded at the reservoirs of the microchannels. Microcontact printing is used to attach this PCTE membrane with PDMS.</p> <p>By using this PCTE membrane, it was possible to intensify the electric field at the interface of two channels while maintaining it low in the other sections of the device. Furthermore, the device also allowed electrophoretic trapping of cells before lysis at a lower applied potential. For instance, it could trap bacteria such as <em>E. coli</em> from a continuous flow into the intersection between two channels for lower electric field (308 V/cm) and lyse the cell when electric field was increased more than 1000 V/cm into that section.</p> <p>Application of lower DC voltage with pressure driven flow alleviated adverse effect from Joule heating. Moreover, gas evolution and bubble generation was not observed during the operation of this device.</p> <p>Accumulation and lysis of bacteria were studied under a fluorescence microscope and quantified by using intensity measurement. To observe the accumulation and lysis, LIVE/DEAD BacLight Bacterial Viability Kit consisting of two separate components of SYTO 9 and propidium iodide (PI) into the cell suspension in addition to GFP expressed <em>E. coli</em> were used. Finally, plate counting was done to determine the efficiency of the device and it was observed that the device could lyse 90% of bacteria for an operation voltage of 300V within 3 min.</p> <p>In conclusion, a robust, reliable and flexible microfluidic cell lysis device was proposed and analyzed which is useful for sample pretreatment in a Micro Total Analysis System.</p> / Master of Applied Science (MASc)

Fatigue testing of stretch sensors often focuses on high amplitude, low-cycle fatigue (LCF) behavior; however, when used for orthopaedic, athletic, or ergonomic assessments, stretch sensors are subjected to low amplitude, high-cycle fatigue (HCF) conditions. As an added layer of complexity, the fatigue testing of stretch sensors is not only focused on the life of the material comprising the sensor, but also on the reliability of the signal produced during the extension and relaxation of the sensor. Research into the development of a smart sock that can be used to measure the range of motion (ROM) of the ankle joint during athletic practices and competitions using stretch sensors is ongoing at Mississippi State University. The current smart sock prototype utilizes StretchSense™ StretchFABRIC capacitive dielectric elastomer sensors. These sensors are no longer manufactured, and FlexSense stretch sensors are being investigated as a potential replacement. To assess the reliability of the signal of the StretchFABRIC sensors currently used in the prototype, two sensors were subjected to 25,000 cycles of fatigue, under with simultaneous capture of the capacitance. The capacitances of the fatigued sensors were then compared to the capacitance of an unfatigued StretchFABRIC sensor during participant trials. Participants completed four static movements and six dynamic gait trials using either the fatigued or unfatigued sensor. Following completion of the initial static and dynamic movements, the movements were repeated using the opposite sensor. Comparison of the fatigued sensor to the unfatigued sensor revealed an upward drift in the capacitance of the fatigued sensor for all trials. Two FlexSense sensors were then subjected to either 450,000 or 250,000 cycles of fatigue with simultaneous capture of the signal from the sensor. To assess the signal, the peak capacitance recorded during the fatigue test was compared to the peak stretch percentage produced by the sensor. The peak displacement remained tight about the mean, while the peak stretch percentage exhibited a high level of scatter. From a materials standpoint, the sensors conformed to the Rabinowitz-Beardmore model of polymer fatigue where an initial monotonic overload of the material is followed by a transition to cyclic stability of the material.

Dynamic testing provides valuable insight into the behavior of materials undergoing fast deformation. During Split-Hopkinson Pressure Bar testing, stress waves are measured using strain gauges as voltage variations that are usually very small. Therefore, an amplifier is required to amplify the data and analyze it. One of the few available amplifiers designed for this purpose is provided by Vishay Micro-Measurements which limits the user’s options when it comes to research or industry. Among the challenges of implementing the Hopkinson technology in the industry are the size and cost of the amplifier. In this work, we propose a novel design of a signal conditioning amplifier that provides the following functionalities: voltage excitation for strain gauges, wide gain range (1-1000), signal balancing, shunting, and filtering. The main objective is to make a smaller and cheaper amplifier that provides equivalent or better performance allowing larger application of the Hopkinson technology in the industry.

This document details the design, implementation, testing, and analysis of an inverted short baseline acoustic positioning system. The system presented here is an above-water, air-based prototype for an underwater acoustic positioning system; it is designed to determine the position of remotely-operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) in the global frame using a method that does not drift over time. A ground-truth positioning system is constructed using a stacked hexapod platform actuator, which mimics the motion of an AUV and provides the true position of an ultrasonic microphone array. An ultrasonic transmitter sends a pulse of sound towards the array; microphones on the array record the pulse of sound and use the time shift between the microphone signals to determine the position of the transmitter relative to the receiver array. The orientation of the array, which is necessary to transform the position estimate to the global frame, is calculated using a Madgwick filter and data from a MEMS IMU. Additionally, a dead reckoning change-in-position estimate is formed using the IMU data. The acoustic position estimate is combined with the dead reckoning estimate using a Kalman filter. The accuracy of this filtered position estimate was verified to 22.1mm within a range of 3.88m in this air-based implementation. The ground-truth positioning system runs on an ESP32 microcontroller using code written in C++, and the acoustic positioning system runs on two STM32 microcontrollers using code written in C. Extrapolation of these results to the underwater regime, as well as recommendations for improving upon this work, are included at the end of the document. All code written for this thesis is available on GitHub and is open-source and well-documented.

The development of a numeric simulation for predicting the performance of an Ocean Current Energy Conversion System is presented in this thesis along with a control system development using a PID controller for the achievement of specified rotational velocity set-points. In the beginning, this numeric model is implemented in MATLAB/Simulink® and it is used to predict the performance of a three phase squirrel single-cage type induction motor/generator in two different cases. The first case is a small 3 meter rotor diameter, 20 kW ocean current turbine with fixed pitch blades, and the second case a 20 meter, 720 kW ocean current turbine with variable pitch blades. Furthermore, the second case is also used for the development of a Voltage Source Variable Frequency Drive for the induction motor/generator. Comparison among the Variable Frequency Drive and a simplified model is applied. Finally, the simulation is also used to estimate the average electric power generation from the 720 kW Ocean Current Energy Conversion System which consists of an induction generator and an ocean current turbine connected with a shaft which modeled as a mechanical vibration system.

Cette thèse explore la reconnaissance de gestes à partir de capteurs inertiels pour Smartphone. Ces gestes consistent en la réalisation d'un tracé dans l'espace présentant une valeur sémantique, avec l'appareil en main. Notre étude porte en particulier sur l'apprentissage de métrique entre signatures gestuelles grâce à l'architecture "Siamoise" (réseau de neurones siamois, SNN), qui a pour but de modéliser les relations sémantiques entre classes afin d'extraire des caractéristiques discriminantes. Cette architecture est appliquée au perceptron multicouche (MultiLayer Perceptron). Les stratégies classiques de formation d'ensembles d'apprentissage sont essentiellement basées sur des paires similaires et dissimilaires, ou des triplets formés d'une référence et de deux échantillons respectivement similaires et dissimilaires à cette référence. Ainsi, nous proposons une généralisation de ces approches dans un cadre de classification, où chaque ensemble d'apprentissage est composé d’une référence, un exemple positif, et un exemple négatif pour chaque classe dissimilaire. Par ailleurs, nous appliquons une régularisation sur les sorties du réseau au cours de l'apprentissage afin de limiter les variations de la norme moyenne des vecteurs caractéristiques obtenus. Enfin, nous proposons une redéfinition du problème angulaire par une adaptation de la notion de « sinus polaire », aboutissant à une analyse en composantes indépendantes non-linéaire supervisée. A l'aide de deux bases de données inertielles, la base MHAD (Multimodal Human Activity Dataset) ainsi que la base Orange, composée de gestes symboliques inertiels réalisés avec un Smartphone, les performances de chaque contribution sont caractérisées. Ainsi, des protocoles modélisant un monde ouvert, qui comprend des gestes inconnus par le système, mettent en évidence les meilleures capacités de détection et rejet de nouveauté du SNN. En résumé, le SNN proposé permet de réaliser un apprentissage supervisé de métrique de similarité non-linéaire, qui extrait des vecteurs caractéristiques discriminants, améliorant conjointement la classification et le rejet de gestes inertiels. / As consumer devices become more and more ubiquitous, new interaction solutions are required. In this thesis, we explore inertial-based gesture recognition on Smartphones, where gestures holding a semantic value are drawn in the air with the device in hand. In our research, speed and delay constraints required by an application are critical, leading us to the choice of neural-based models. Thus, our work focuses on metric learning between gesture sample signatures using the "Siamese" architecture (Siamese Neural Network, SNN), which aims at modelling semantic relations between classes to extract discriminative features, applied to the MultiLayer Perceptron. Contrary to some popular versions of this algorithm, we opt for a strategy that does not require additional parameter fine tuning, namely a set threshold on dissimilar outputs, during training. Indeed, after a preprocessing step where the data is filtered and normalised spatially and temporally, the SNN is trained from sets of samples, composed of similar and dissimilar examples, to compute a higher-level representation of the gesture, where features are collinear for similar gestures, and orthogonal for dissimilar ones. While the original model already works for classification, multiple mathematical problems which can impair its learning capabilities are identified. Consequently, as opposed to the classical similar or dissimilar pair; or reference, similar and dissimilar sample triplet input set selection strategies, we propose to include samples from every available dissimilar classes, resulting in a better structuring of the output space. Moreover, we apply a regularisation on the outputs to better determine the objective function. Furthermore, the notion of polar sine enables a redefinition of the angular problem by maximising a normalised volume induced by the outputs of the reference and dissimilar samples, which effectively results in a Supervised Non-Linear Independent Component Analysis. Finally, we assess the unexplored potential of the Siamese network and its higher-level representation for novelty and error detection and rejection. With the help of two real-world inertial datasets, the Multimodal Human Activity Dataset as well as the Orange Dataset, specifically gathered for the Smartphone inertial symbolic gesture interaction paradigm, we characterise the performance of each contribution, and prove the higher novelty detection and rejection rate of our model, with protocols aiming at modelling unknown gestures and open world configurations. To summarise, the proposed SNN allows for supervised non-linear similarity metric learning, which extracts discriminative features, improving both inertial gesture classification and rejection.

Monitoring the mechanical integrity of critical structures is extremely important, as mechanical defects can potentially have adverse impacts on their safe operability throughout their service life. Structural defects can be detected by using active structural health monitoring (SHM) approaches, in which a given structure is excited with harmonic mechanical waves generated by actuators. The response of the structure is then collected using sensor(s) and is analyzed for possible defects, with various active SHM approaches available for analyzing the response of a structure to single- or multi-frequency harmonic excitations. In order to identify the appropriate excitation frequency, however, the majority of such methods require a priori knowledge of the characteristics of the defects under consideration. This makes the whole enterprise of detecting structural defects logically circular, as there is usually limited a priori information about the characteristics and the locations of defects that are yet to be detected. Furthermore, the majority of SHM techniques rely on sensors for response collection, with the very same sensors also prone to structural damage. The Surface Response to Excitation (SuRE) method is a broadband frequency method that has high sensitivity to different types of defects, but it requires a baseline. In this study, initially, theoretical justification was provided for the validity of the SuRE method and it was implemented for detection of internal and external defects in pipes. Then, the Comprehensive Heterodyne Effect Based Inspection (CHEBI) method was developed based on the SuRE method to eliminate the need for any baseline. Unlike traditional approaches, the CHEBI method requires no a priori knowledge of defect characteristics for the selection of the excitation frequency. In addition, the proposed heterodyne effect-based approach constitutes the very first sensor-less smart monitoring technique, in which the emergence of mechanical defect(s) triggers an audible alarm in the structure with the defect. Finally, a novel compact phased array (CPA) method was developed for locating defects using only three transducers. The CPA approach provides an image of most probable defected areas in the structure in three steps. The techniques developed in this study were used to detect and/or locate different types of mechanical damages in structures with various geometries.