<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Massieh Najafi</style></author><author><style face="normal" font="default" size="100%">David M. Auslander</style></author><author><style face="normal" font="default" size="100%">Peter L. Bartlett</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Michael D. Sohn</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling and Measurement Constraints in Fault Diagnostics for HVAC Systems</style></title><secondary-title><style face="normal" font="default" size="100%">ASME Journal of Dynamic Systems, Measurement, and Controls</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many studies have shown that energy savings of five to fifteen percent are achievable in commercial buildings by detecting and correcting building faults, and optimizing building control systems. However,in spite of good progress in developing tools for determining HVAC diagnostics, methods to detect faults in HVAC systems are still generally undeveloped. Most approaches use numerical filtering or parameter estimation methods to compare data from energy meters and building sensors to predictions from mathematical or statistical models. They are effective when models are relatively accurate and data contain few errors. In this paper, we address the case where models are imperfect and data are variable, uncertain, and can contain error. We apply a Bayesian updating approach that is systematic in managing and accounting for most forms of model and data errors. The proposed method uses both knowledge of first principle modeling and empirical results to analyze the system performance within the boundaries defined by practical constraints. We demonstrate the approach by detecting faults in commercial building air handling units. We find that the limitations that exist in air handling unit diagnostics due to practical constraints can generally be effectively addressed through the proposed approach.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Massieh Najafi</style></author><author><style face="normal" font="default" size="100%">David M. Auslander</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Michael D. Sohn</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Statistical Pattern Analysis Framework for Rooftop Unit Diagnostics</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Heating, Ventilating, Air-Conditioning and Refrigeration Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>