Detection and Diagnosis of HVAC Faults via Electrical Load Monitoring

Detection and diagnosis of faults (FDD) in HVAC equipment have typically relied on measurements of variables available to a control system, including temperatures, flows, pressures, and actuator control signals. Electrical power at the level of a fan, pump, or chiller has been generally ignored because power meters are rarely installed at individual loads. This paper presents two techniques for using electrical power data for detecting and diagnosing a number of faults in air-handling units. The results from the two techniques are compared and the situation for which each is applicable is assessed. One technique relies on gray-box correlations of electrical power with such exogenous variables as airflow or motor speed. This technique has been implemented with short-term average electrical power measured by dedicated submeters. With somewhat reduced resolution, it has also been implemented with a high-speed, centralized power meter that provides component-specific power information via analysis of the step changes in power that occur when a given device turns on or off. This technique was developed to detect and diagnose a limited number of air handler faults and is shown to work well with data taken from a test building. A detailed evaluation of the method is presented in the companion paper, which documents the results of a series of semiblind tests. The second technique relies on physical models of the electromechanical dynamics that occur immediately after a motor is turned on. This technique has been demonstrated with submetered data for a pump and for a fan. Tests showed that several faults could be successfully detected from motor startup data alone. While the method relies solely on generally stable and accurate voltage and current sensors, thereby avoiding problems with flow and temperature sensors used in other fault detection methods, it requires electrical data taken directly at the motor, downstream of variable-speed drives, where current sensors would not be installed for control or load-monitoring purposes.

[1]  Margaret F. Fels PRISM: An Introduction , 1986 .

[2]  Masami Suzuki,et al.  Typical faults of air conditioning systems and fault detection by ARX model and extended Kalman filter , 1996 .

[3]  Kazuyuki Kamimura,et al.  Chiller condition monitoring using topological case-based modeling , 1996 .

[4]  Timothy I. Salsbury Fault detection and diagnosis in HVAC systems using analytical models , 1996 .

[5]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[6]  H. C. Peitsman,et al.  Application of black-box models to HVAC systems for fault detection , 1996 .

[7]  L. K. Norford,et al.  Non-Intrusive Electric Load Monitoring in Commercial Buildings , 1992 .

[8]  J. Braun,et al.  Performance and control characteristics of a large cooling system , 1987 .

[9]  David E. Claridge,et al.  A Four-Parameter Change-Point Model for Predicting Energy Consumption in Commercial Buildings , 1992 .

[10]  G. E. Kelly,et al.  Fault diagnosis of an air-handling unit using artificial neural networks , 1996 .

[11]  J. C. Visier,et al.  Development of a fault diagnosis method for heating systems using neural networks , 1996 .

[12]  John E. Seem,et al.  Leave the Outdoor Air Damper Wide Open , 1998 .

[13]  J House,et al.  An Expert Rule Set For Fault Detection In Air-Handling Units | NIST , 2001 .

[14]  Philip Haves,et al.  Condition monitoring in HVAC subsystems using first principles models , 1996 .

[15]  Rolf Johansson,et al.  System modeling and identification , 1993 .

[16]  Steven B. Leeb,et al.  A conjoint pattern recognition approach to nonintrusive load monitoring , 1993 .

[17]  Roger Owen Hill,et al.  Applied change of mean detection techniques for HVAC fault detection and diagnosis and power monitoring , 1995 .

[18]  Mark DeSimone,et al.  A standard simulation testbed for the evaluation of control algorithms & strategies related to variable air volume HVAC systems , 1995 .

[19]  Jonathan A. Wright,et al.  Demonstration of Fault Detection and Diagnosis Methods for Air-Handling Units , 2002 .

[20]  Steven B. Leeb,et al.  Instrumentation for High Performance Nonintrusive Electrical Load Monitoring , 1998 .

[21]  David E. Claridge,et al.  A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model , 1993 .

[22]  S. R. Shaw,et al.  Transient event detection in spectral envelope estimates for nonintrusive load monitoring , 1995 .

[23]  Arthur L. Dexter,et al.  Fault Diagnosis in Air-Conditioning Systems: A Multi-Step Fuzzy Model-Based Approach , 2001 .