Improvement of belt tension monitoring in a belt-driven automated material handling system
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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.