Share

IIR document

Model-based fault detection and diagnosis for HVAC.

Author(s) : LIANG J., DU R.

Summary

Recently intelligent preventive maintenance is gaining more and more interests for HVAC systems. The key to preventive maintenance is a cost-effective fault detection and diagnosis (FDD) method. This paper presents a model-based FDD method. First, the model of a single zone HVAC system is developed. Based on the model, the characteristics of several faults (cooling coi1 fouling/blockage, re-circulation damper malfunction and supply fan speed decrease) are investigated. Based on computer simulation, it is shown that by analysing the variation of the states of the system, these faults can be detected. Furthermore, a neural network classifier is developed to recognize the faults. This model based fault detection and diagnosis can help to maintain the health of the HVAC systems, improve the indoor thermal comfort level and save energy.

Available documents

Format PDF

Pages: 2006-1

Available

  • Public price

    20 €

  • Member price*

    Free

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: Model-based fault detection and diagnosis for HVAC.
  • Record ID : 2007-0777
  • Languages: English
  • Source: Innovative Equipment and Systems for Comfort and Food Preservation.
  • Publication date: 2006/02/16

Links


See other articles from the proceedings (51)
See the conference proceedings

Indexing