Evaluating the performance of automated fault detection and diagnosis tools

David Yuill

Research output: Chapter in Book/Report/Conference proceedingChapter


Automated fault detection and diagnosis (AFDD) has the potential to provide early warning of performance degradation faults before they might otherwise be apparent, and before they cause failure of the system. AFDD approaches have been developed in many industries, such as aerospace, process control, and air-conditioning. In air-conditioning applications the cost-sensitivity of the market requires that there is minimal cost premium for AFDD, so methods typically must be deployed with very few sensors to provide input and minimal engineering cost. In addition, because life safety is not a concern, less accurate methods can be tolerated. In this landscape, there are many methods that don’t perform well, but until recently there has been no standardized method or metrics to test or describe performance of AFDD. This chapter describes a new methodology, and a specific method to test and characterize the performance of AFDD tools that are applied to air-conditioning systems, and illustrates the methods with a case study. The widely used AFDD approach tested in the case study shows poor performance, which underscores the importance of evaluating AFDD performance.

Original languageEnglish (US)
Title of host publicationAutomotive Air Conditioning
Subtitle of host publicationOptimization, Control and Diagnosis
PublisherSpringer International Publishing
Number of pages15
ISBN (Electronic)9783319335902
ISBN (Print)9783319335896
StatePublished - Jan 1 2016


  • Air-conditioning system
  • Building
  • Fault detection
  • Fault isolation

ASJC Scopus subject areas

  • General Engineering


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