<?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>10</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of Machine Learning in Fault Diagnostics of Mechanical Systems</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Modeling, Simulation and Control</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2008</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault Diagnostics and Supervised Testing: How Fault Diagnostic tools can be Proactive?</style></title><secondary-title><style face="normal" font="default" size="100%">Eleventh International Conference on Intelligent Systems and Controls</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=503</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Overcoming the Complexity of Diagnostic Problems due to Sensor Network Architecture</style></title><secondary-title><style face="normal" font="default" size="100%">Eleventh International Conference on Intelligent Systems and Controls</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/08</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=503</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system&#039;s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.&lt;/p&gt;</style></abstract></record></records></xml>