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No description available.

No description available.

No description available.

No description available.

This thesis presents the theoretical analysis and experiment results of MEMS sensors designed for the application of low frequency vibration sensing. Each sensor consists of a proof mass connected to a folded beam micro-flexure, with an attached capacitive comb drive for displacement sensing. Three comb drive arrangements are evaluated, the transverse, lateral, and tri-plate differential. The sensors are fabricated using the well developed foundry processes of PolyMUMPS and SoiMUMPS. In addition, a capacitance to voltage readout circuit is fabricated using discrete components. Static tests, evaluating the capacitance to displacement relation, are conducted on a six degree of freedom robotic manipulator, and dynamic tests evaluating the sensor response to sinusoidal excitations are conducted on a vibrating beam. The end use of the sensor involves real-time vibration monitoring of automobile mechanical systems, such as power seats, windshield wipers, mirrors, trunks, and windows, allowing for early detection of mechanical faults before catastrophic failure.

This thesis presents the theoretical analysis and experiment results of MEMS sensors designed for the application of low frequency vibration sensing. Each sensor consists of a proof mass connected to a folded beam micro-flexure, with an attached capacitive comb drive for displacement sensing. Three comb drive arrangements are evaluated, the transverse, lateral, and tri-plate differential. The sensors are fabricated using the well developed foundry processes of PolyMUMPS and SoiMUMPS. In addition, a capacitance to voltage readout circuit is fabricated using discrete components. Static tests, evaluating the capacitance to displacement relation, are conducted on a six degree of freedom robotic manipulator, and dynamic tests evaluating the sensor response to sinusoidal excitations are conducted on a vibrating beam. The end use of the sensor involves real-time vibration monitoring of automobile mechanical systems, such as power seats, windshield wipers, mirrors, trunks, and windows, allowing for early detection of mechanical faults before catastrophic failure.

A novel model was developed to test the use of short data sets for testing various alarm thresholds as part of an energy management program. Several years of 15-minute interval data were utilized from five buildings in Jacksonville, Florida. The model aggregated the data by day type and occupancy so that there were four period types used. For all of the buildings’ meters, their daily usage by period type was tested against the threshold to determine if an alarm would be triggered, which would then be assigned a reward and cost based upon the type and duration of response. The risk management value was converted to dollars, in order to normalize the energy and time. It was determined that the 5-month short data set was the most appropriate choice for short data sets. In addition, it was concluded that the thresholds should be set between 0.8 and 1.0 standard deviation above the average of the short window. Several recommendations for further study are also enclosed.

More than $18 billion of energy is wasted annually in the U.S. commercial building sector. Retro-Commissioning services have proven to be successful with relatively short payback times, but tools that support the commissioning effort in maintaining the optimal energy performance in a building are just not readily available. The current work in the field of fault detection and diagnostics of HVAC systems, its cost, complexity and reliance on improved sensor technology, will require years until it can become the mainstay in building energy management. In the meantime, a simplified system is needed today that can be robust and universal enough to use in most types of buildings, address the main concerns of building owners by focusing on consumption deviations that significantly affect the bottom line and provide them some assistance in the remediation of these problems. This thesis presents the results of the development and testing of an advanced prototype of the Automated Building Commissioning Analysis Tool (ABCAT), which has detected three significant energy consumption deviations through four live building implementations. The ABCAT has also demonstrated additional functional benefits of tracking the savings due to retro-commissioning efforts, verifying billed utility data in addition to its primary function of detecting significant consumption faults. Although similar attempts have been made in FDD at the whole building level, the simplification, flexibility, robustness and benefits of this new approach are expected to exhibit the characteristics that will be desired and desperately needed by industry professionals.

Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined if the vehicle is not on time. Supervised classification methods for detecting faults in vehicles are compared in this thesis. Data has been collected by a vehicle manufacturer including three kinds of faulty states in vehicles (i.e. charge air cooler leakage, radiator and air filter clogging). The problem consists of differentiating between the normal data and the three different categories of faulty data. Evaluated methods include linear model, neural networks model, 1-nearest neighbor and random forest model. For every kind of model, a variable selection method should be used. In our thesis we try to find the best model for this problem, and also select the most important input signals. After we compare these four models, we found that the best accuracy (96.9% correct classifications) was achieved with the random forest model.