Fault detection in commercial building VAV AHU: A case study of an academic building
| Autor: | Leon R. Glicksman, Suhrid Deshmukh, Leslie Norford, Stephen Samouhos |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Building management system
Primary energy Computer science business.industry 020209 energy Mechanical Engineering 0211 other engineering and technologies 02 engineering and technology Building and Construction Energy consumption Fault (power engineering) Fault detection and isolation Damper Reliability engineering 021105 building & construction HVAC 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Air handler business Efficient energy use Civil and Structural Engineering |
| Zdroj: | Energy and Buildings. 201:163-173 |
| ISSN: | 0378-7788 |
| DOI: | 10.1016/j.enbuild.2019.06.051 |
| Popis: | The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. Estimates suggest that between 5% and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected faults in air handling units (AHU) that cause large building energy inefficiencies. Algorithm for faults like stuck dampers and leaking dampers have been developed and tested in this particular case study. These damper fault detection algorithms can be applied to outdoor air and return air dampers both. The technical contributions of this work include expert rules that adapt to the Heating, Ventilation and Air-Conditioning (HVAC) equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. The combination of expert-rule based fault detection combined with first principles thermodynamic modeling is a unique contribution of this work that leads to quicker fault detection with minimal non-intrusive measurements. These contributions are particularly unique in their treatment of models and the careful consideration of user-interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as stuck dampers and imbalanced airflows within several large air-handling units were targeted. The algorithms focused on detecting faults with minimal data and non-intrusive measurements. An example is presented of the potential energy savings in a large academic building that has been monitored. Real data from an academic building in Boston was collected from the building energy management system (BEMS) for the purpose of this study. Savings of around $3400/month or 18% of the monthly cost when a stuck damper fault occurred over an entire month in an air handler. Another fault that was investigated was that of leaking dampers which lead to savings of approximately $500/month or around 2.5% of the monthly energy expense. The savings would accrue if the fault were corrected; otherwise the occurrence of the fault causes a waste of money and energy predicted. The algorithms developed can be applied to large commercial office buildings, academic buildings, hospitals and other modern buildings in various climates as long as they have data collecting capabilities. |
| Databáze: | OpenAIRE |
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