Research Finding: Automated Fault Detection and Diagnostics in Rooftop Units: Impact Evaluation and Optimal Service Scheduling


Publication Date: April 2015

RTUs serve 60% of commercial floor space and account for about 150 Terawatt hours of annual electrical usage (~1.56 Quads of primary energy) and about $15B in electric bills in the US.

The key to a successful automated fault detection and diagnostics (AFDD) system is to provide the user with actionable information. Historically, actionable information has an alert that a fault has been detected and identified. This is often inadequate information to trigger a repair in the small and medium-sized commercial building market. The missing element is the financial information to determine a return on investment for the particular maintenance task. This is especially true if the particular fault does not impact the comfort of the building occupants.

By choosing to perform service on an air-conditioner when it makes the most economic sense, it is possible to reduce utility and equipment cost impacts by incurring some additional service costs instead. CBEI results, to date, support the concept of using fault detection, diagnostics, and fault impact isolation to determine total energy and run time impacts and costing to deliver automated service recommendations.

Major customers of HVAC equipment and systems for commercial buildings are interested in HVAC equipment with advanced diagnostics that minimize energy and maintenance costs and extend equipment life. Advanced RTU fault detection and diagnostics is critical to building energy savings and extending equipment lifespan.

CBEI Accomplishments:

  • Developed the overall virtual sensor package based AFDD system for RTUs.
  • Developed and validated fault impact estimation and isolation methodologies.
  • Developed optimal service recommendation methodology for simultaneous fault scenarios.
  • Implemented the performance degradation method under lab conditions on an advanced RTU.
  • Obtained lab test results for single and multiple faults conditions.
  • Performed performance degradation analysis of single injected faults conditions including, condenser blockage, liquid line constraint, and compressor leakage on an advanced RTU with some dual fault scenarios.

Related Publications:

  1. Li, H. and Braun, J.E. “A Methodology for Diagnosing Multiple-Simultaneous Faults in Vapor Compression Air Conditioners,” HVAC&R Research, Vol. 13, No. 2, Pages 369-395, 2007
  2. Li, H. and Braun, J.E.  “Decoupling Features and Virtual Sensors for Diagnosis of Faults in Vapor Compression Air Conditioners,” International Journal of Refrigeration, Vol. 30, No. 3, Pages 546-564, 2007.
  3. Kim, W., Braun, J.E., “Extension of a Virtual Refrigerant Charge Sensor,” Vol. 55, Pages 224-235, International Journal of Refrigeration, 2015
  4. Kim, W. and Braun, J.E., “Development and Evaluation of Virtual Refrigerant Mass Flow Sensors for Fault Detection and Diagnostics,” In Review, International Journal of Refrigeration, 2015.
  5. Hjortland, A.L. and Braun, J.E., “Virtual Sensors for RTU Air-Side Diagnostics,” Accepted for Publication in Science and Technology for the Built Environment, 2015.

Publication Title: Research Finding: Automated Fault Detection and Diagnostics in Rooftop Units: Impact Evaluation and Optimal Service Scheduling

Consortium Member(s): Purdue University, United Technology Research Center

Project Contact: Jim Braun, Purdue and Mikhail Gorbounov, UTRC

Date: April 14, 2015