Experimental study on electrical signatures of common faults for packaged DX rooftop units

Abstract Fault detection and diagnosis (FDD) is an effective way to automatically detect the existing faults of a rooftop unit (RTU) and increase the reliability and availability of system. Most existing FDD methods use thermodynamic signals as fault indicators. Recently, however, major concerns have arisen in regards to whether refrigerant-related temperature sensors and intrusive pressure measurements are reliable. Electrical signal measurements provide plenty of information about a system but require the use of an electrical meter. One way to solve this issue is to install variable frequency drives (VFDs) on the HVAC system, since VFDs not only optimize the system performance, but also provide electrical parameter readings. In this study, a series of lab experiments were conducted to investigate the relationship between the electrical signals and common faults. Since environmental influences have been correlated with the system performance, the impact of several driving conditions on the electrical signals was also investigated. This study identified an electrical signature for each fault and individually separated the common faults based on three parameters: the fan power, compressor power, and the supply air temperature. The results of the study are of use to the development and implementation of a new FDD method based on combining the electrical and temperature parameter measurements.

[1]  Zhimin Du,et al.  A novel model-based fault detection method for temperature sensor using fractal correlation dimensio , 2011 .

[2]  Haitao Wang,et al.  A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings , 2011 .

[3]  Angel Gerardo Rodriguez,et al.  Effect of Refrigerant Charge, Duct Leakage, and Evaporator Air Flow on the High Temerature Performance of Air Conditioners and Heat Pumps , 1995 .

[4]  Joaquín Navarro-Esbrí,et al.  A vapour compression chiller fault detection technique based on adaptative algorithms. Application to on-line refrigerant leakage detection , 2006 .

[5]  K. Roth,et al.  Residential Central AC Fault Detection & Diagnostics , 2006 .

[6]  Savvas A. Tassou,et al.  Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems. , 2005 .

[7]  Piotr A. Domanski,et al.  Performance of a residential heat pump operating in the cooling mode with single faults imposed , 2009 .

[8]  Youming Chen,et al.  A fault detection technique for air-source heat pump water chiller/heaters , 2009 .

[9]  James E. Braun,et al.  A Methodology for Diagnosing Multiple-Simultaneous Faults in Rooftop Air Conditioners , 2004 .

[10]  Hua Han,et al.  Automated FDD of multiple-simultaneous faults (MSF) and the application to building chillers , 2011 .

[11]  Zhimin Du,et al.  Detection and diagnosis for multiple faults in VAV systems , 2007 .

[12]  James E. Braun,et al.  Decoupling features and virtual sensors for diagnosis of faults in vapor compression air conditioners , 2007 .

[13]  James E. Braun,et al.  Common faults and their impacts for rooftop air conditioners , 1998 .

[14]  Frédéric Magoulès,et al.  Development of an RDP neural network for building energy consumption fault detection and diagnosis , 2013 .

[15]  Steven B. Leeb,et al.  Detection and Diagnosis of HVAC Faults via Electrical Load Monitoring , 2002 .

[16]  Piotr A. Domanski,et al.  Residential Heat Pump Heating Performance with Single Faults Imposed , 2011 .

[17]  Steven B. Leeb,et al.  Detection of Rooftop Cooling Unit Faults Based on Electrical Measurements , 2006 .

[18]  James E. Braun,et al.  The impact of fouling on the performance of filter–evaporator combinations , 2007 .

[19]  Yong Chan Kim,et al.  The effects of improper refrigerant charge on the performance of a heat pump with an electronic expansion valve and capillary tube , 2002 .

[20]  Shengwei Wang,et al.  Valve fault detection and diagnosis based on CMAC neural networks , 2004 .

[21]  Jian-Qiao Sun,et al.  A top-down strategy with temporal and spatial partition for fault detection and diagnosis of buildin , 2011 .

[22]  James E. Braun.  Automated Fault Detection and Diagnostics for Vapor Compression Cooling Equipment , 2003 .

[23]  Steven B. Leeb,et al.  FAULT DETECTION BASED ON MOTOR START TRANSIENTS AND SHAFT HARMONICS MEASURED AT THE RTU ELECTRICAL SERVICE , 2004 .

[24]  Steven T. Bushby,et al.  A rule-based fault detection method for air handling units , 2006 .

[25]  James E. Braun,et al.  A Methodology for Diagnosing Multiple Simultaneous Faults in Vapor-Compression Air Conditioners , 2007 .

[26]  James E. Braun,et al.  Evaluating the Performance of a Fault Detection and Diagnostic System for Vapor Compression Equipment , 1998 .