Browsing by Author "Musselman, Marcus William"
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Item Improvement of belt tension monitoring in a belt-driven automated material handling system(2010-08) Musselman, Marcus William; Djurdjanovic, Dragan; Wilson, Preston S.The goal of the study presented in this thesis was the improvement of estimation and monitoring procedures for condition monitoring of belt tension and misalignment in belt-driven automated material handling systems widely used in modern semiconductor manufacturing systems. In pursuit of this goal, two 3-factor, 3-level experiments were designed to study how belt vibration characteristics depend on changes in belt length, belt tension, belt misalignment, and initial location of the excitation of belt vibration. Dependent variables in each of the experiments were drawn from a denoised frequency spectrum calculated from an Autoregressive model of the belt vibration time-series. A feature vector was developed from the Autoregressive features via variance based sensitivity analysis. Results showed that belt vibration characteristics were sensitive to changes in all of the independent variables examined. These results motivated the design of a device to improve the standardized technique widely used to monitor belt tension in belt-driven material handling systems. Reducing variance in the belt length and the location of the initial excitation of belt vibration yielded a reduction of tension estimate standard deviation an order of magnitude, as compared to a human performing the standardized technique. Thus, the use of this device provided higher belt tension estimate resolution. Future work that could lead to a less intrusive technique is presented.Item Monitoring of biomedical systems using non-stationary signal analysis(2013-12) Musselman, Marcus William; Djurdjanovic, DraganMonitoring of engineered systems consists of characterizing the normal behavior of the system and tracking departures from it. Techniques to monitor a system can be split into two classes based on their use of inputs and outputs of the system. Systems-based monitoring refers to the case when both inputs and outputs of a system are available and utilized. Conversely, symptomatic monitoring refers to the case when only outputs of the system are available. This thesis extended symptomatic and systems-based monitoring of biomedical systems via the use of non-stationary signal processing and advanced monitoring methods. Monitoring of various systems of the human body is encumbered by several key hurdles. First, current biomedical knowledge may not fully comprehend the extent of inputs and outputs of a particular system. In addition, regardless of current knowledge, inputs may not be accessible and outputs may be, at best, indirect measurements of the underlying biological process. Finally, even if inputs and outputs are measurable, their relationship may be highly nonlinear and convoluted. These hurdles require the use of advanced signal processing and monitoring approaches. Regardless of the pursuit of symptomatic or system-based monitoring, the aforementioned hurdles can be partially overcome by using non-stationary signal analysis to reveal the way frequency content of biomedical signals change over time. Furthermore, the use of advanced classification and monitoring methods facilitated reliable differentiation between various conditions of the monitored system based on the information from non-stationary signal analysis. The human brain was targeted for advancement of symptomatic monitoring, as it is a system responding to a plethora internal and external stimuli. The complexity of the brain makes it unfeasible to realize system-based monitoring to utilize all the relevant inputs and outputs for the brain. Further, measurement of brain activity (outputs), in the indirect form of electroencephalogram (EEG), remains a workhorse of brain disorder diagnosis. In this thesis, advanced signal processing and pattern recognition methods are employed to devise and study an epilepsy detection and localization algorithm that outperforms those reported in literature. This thesis also extended systems-based monitoring of human biomedical systems via advanced input-output modeling and sophisticated monitoring techniques based on the information from non-stationary signal analysis. Explorations of system-based monitoring in the NMS system were driven by the fact that joint velocities and torques can be seen NMS responses to electrical inputs provided by the central nervous system (CNS) and the electromyograph (EMG) provides an indirect measurement of CNS excitations delivered to the muscles. Thus, both inputs and outputs of this system are more or less available and one can approach its monitoring via the use of system-based approaches.