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Model-based fault detection and diagnosis for HVAC systems.

Detección de fallos mediante modelo y diagnosis de los sistemas HVAC.

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

Type of article: Article

Summary

This article is the Spanish translation of a paper presented at the IIR meeting in Auckland, New Zealand (see the Bulletin of the IIR, reference 2007-0777). 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.

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Format PDF

Pages: pp. 66-74 (6 p.)

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Details

  • Original title: Detección de fallos mediante modelo y diagnosis de los sistemas HVAC.
  • Record ID : 2010-1981
  • Languages: Spanish
  • Source: IIF-IIR/Frío Calor Aire acond. - vol. 35 - n. 398
  • Publication date: 2007/12

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