Hjortland, Andrew L. M.S.M.E, Purdue University, May 2014. Probabilistic Fault Detection and Diagnostics for Packaged Air-Conditioner Outdoor-Air Economizers. Major Professor: James E. Braun, School of Mechanical Engineering. Approximately 60% of commercial floor space in the US is served by rooftop airconditioners (RTUs), many of which utilize outdoor-air economizers to reduce building energy consumption [1, 2]. However, preventative maintenance in the field is uncommon for these types of units and service calls are generally only made during emergencies. Because of this, it is not uncommon for faults to persist in RTUs unnoticed, decreasing system efficiency and increasing run-time and operating costs. The result of these faults leads to an additional 15% to 30% energy consumption in commercial cooling equipment, according to some studies [3, 4]. Poor economizer control, economizer damper failure, and excess outdoor-air contribute to these performance degradations. In order to promote optimal RTU performance and reduce operating costs, an automated fault detection and diagnostics (AFDD) tool has been designed for RTUs with integrated economizers. Based on previously proposed methods, the proposed method advances the economizer fault detection and diagnosis components by using statistical classifiers in order to provide more robust, probabilistic fault outputs. A set of air-side virtual sensors has also been added to the method in order to expand the applicable range of conditions fault detection and diagnostics can be applied. The proposed method is designed to be integrated into the RTU controller during manufacturing. An integrated approach was pursued in order to increase the fault detection sensitivity and to decrease the fault alarm rate by training normal performance models using laboratory test data collected using psychrometric chambers. This method is promising for RTUs since these systems are mass produced and the normal performance of one unit is representative of entire family of units.
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