TY - JOUR
T1 - Application of machine learning in the fault diagnostics of air handling units
JF - Applied Energy
Y1 - 2012/08//
SP - 347
EP - 358
A1 - Massieh Najafi
A1 - David M. Auslander
A1 - Peter L. Bartlett
A1 - Philip Haves
A1 - Michael D. Sohn
KW - Air-handling unit
KW - Bayesian network
KW - energy management
KW - fault detection and diagnosis
KW - HVAC systems
KW - Machine learning
AB - An air handling unit’s energy usage can vary from the original design as components fail or fault – dampers leak or fail to open/close, valves get stuck, and so on. Such problems do not necessarily result in occupant complaints and, consequently, are not even recognized to have occurred. In spite of recent progress in the research and development of diagnostic solutions for air handling units, there is still a lack of reliable, scalable, and affordable diagnostic solutions for such systems. Modeling limitations, measurement constraints, and the complexity of concurrent faults are the main challenges in air handling unit diagnostics. The focus of this paper is on developing diagnostic algorithms for air handling units that can address such constraints more effectively by systematically employing machine-learning techniques. The proposed algorithms are based on analyzing the observed behavior of the system and comparing it with a set of behavioral patterns generated based on various faulty conditions. We show how such a pattern-matching problem can be formulated as an estimation of the posterior distribution of a Bayesian probabilistic model. We demonstrate the effectiveness of the approach by detecting faults in commercial building air handling units.
VL - 96
JO - Applied Energy
DO - 10.1016/j.apenergy.2012.02.049
ER -
TY - CONF
T1 - Modeling Transient Contaminant Transport in HVAC Systems and Buildings
T2 - Indoor Air 2002, June 30 - July 5, 2002
Y1 - 2002/
SP - 217
EP - 222
A1 - Clifford C. Federspiel
A1 - Huilin Li
A1 - David M. Auslander
A1 - David M. Lorenzetti
A1 - Ashok J. Gadgil
KW - Air transport
KW - hvac
KW - Modeling pollutant concentrations
AB - A mathematical model of the contaminant transport in HVAC systems and buildings is described. The model accounts for transients introduced by control elements such as fans and control dampers. The contaminant transport equations are coupled to momentum equations and mass continuity equations of the air. To avoid modeling variable transport delays directly, ducts are divided into a large number of small sections. Perfect mixing is assumed in each section. Contaminant transport equations are integrated with momentum equations in a way that guarantees mass continuity by using two non-negative velocities for computing the mass transport between elements. Computer simulations illustrate how the model may be used to analyze and design control systems that respond to a sudden release of a toxic contaminant near a building. By coupling transient flow prediction with transient contaminant prediction, the model overcomes a number of problems with existing contaminant transport codes.
JF - Indoor Air 2002, June 30 - July 5, 2002
CY - Monterey, California
U2 - LBNL-49603
ER -