Heat recovery unit failure detection in air handling unit

Manik Madhikermi*, Narges Yousefnezhad, Kary Främling

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

7 Citations (Scopus)
115 Downloads (Pure)


Maintenance is a complicated task that encompasses various activities including fault detection, fault diagnosis, and fault reparation. The advancement of Computer Aided Engineering (CAE) has increased challenges in maintenance as modern assets have became complex mixes of systems and sub systems with complex interaction. Among maintenance activities, fault diagnosis is particularly cumbersome as the reason of failures on the system is often neither obvious in terms of their source nor unique. Early detection and diagnosis of such faults is turning to one of the key requirements for economical and functional asset efficiency. Several methods have been investigated to detect machine faults for a number of years that are relevant for many application domains. In this paper, we present the process history-based method adopting nominal efficiency of Air Handling Unit (AHU) to detect heat recovery failure using Principle Component Analysis (PCA) in combination of the logistic regression method.

Original languageEnglish
Title of host publicationAdvances in Production Management Systems. Smart Manufacturing for Industry 4.0 - IFIP WG 5.7 International Conference, APMS 2018, Proceedings
Number of pages8
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Advances in Production Management Systems - Seoul, Korea, Republic of
Duration: 26 Aug 201830 Aug 2018

Publication series

NameIFIP Advances in Information and Communication Technology
ISSN (Print)1868-4238


ConferenceInternational Conference on Advances in Production Management Systems
Abbreviated titleAPMS
Country/TerritoryKorea, Republic of
Internet address


  • Air handling unit
  • Fault detection
  • Fault diagnosis
  • FDD
  • Heat recovery unit
  • Logistic regression
  • PCA


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