Explainable artificial intelligence based heat recycler fault detection in air handling unit

Manik Madhikermi, Avleen Kaur Malhi*, Kary Främling

*Corresponding author for this work

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

Abstract

We are entering a new age of AI applications where machine learning is the core technology but machine learning models are generally non-intuitive, opaque and usually complicated for people to understand. The current AI applications inability to explain is decisions and actions to end users have limited its effectiveness. The explainable AI will enable the users to understand, accordingly trust and effectively manage the decisions made by machine learning models. The heat recycler’s fault detection in Air Handling Unit (AHU) has been explained with explainable artificial intelligence since the fault detection is particularly burdensome because the reason for its failure is mostly unknown and unique. The key requirement of such systems is the early diagnosis of such faults for its economic and functional efficiency. The machine learning models, Support Vector Machine and Neural Networks have been used for the diagnosis of the fault and explainable artificial intelligence has been used to explain the models’ behaviour.

Original languageEnglish
Title of host publicationExplainable, Transparent Autonomous Agents and Multi-Agent Systems - 1st International Workshop, EXTRAAMAS 2019, Revised Selected Papers
EditorsDavide Calvaresi, Michael Schumacher, Amro Najjar, Kary Främling
Pages110-125
Number of pages16
DOIs
Publication statusPublished - 11 Sep 2019
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Explainable Transparent Autonomous Agents and Multi-Agent Systems - Montreal, Canada
Duration: 13 May 201914 May 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11763 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

WorkshopInternational Workshop on Explainable Transparent Autonomous Agents and Multi-Agent Systems
Abbreviated titleEXTRAAMAS
CountryCanada
CityMontreal
Period13/05/201914/05/2019

Keywords

  • Explainable artificial intelligence
  • Heat recycler unit
  • Neural networks
  • Support vector machine

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  • Cite this

    Madhikermi, M., Malhi, A. K., & Främling, K. (2019). Explainable artificial intelligence based heat recycler fault detection in air handling unit. In D. Calvaresi, M. Schumacher, A. Najjar, & K. Främling (Eds.), Explainable, Transparent Autonomous Agents and Multi-Agent Systems - 1st International Workshop, EXTRAAMAS 2019, Revised Selected Papers (pp. 110-125). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11763 LNAI). https://doi.org/10.1007/978-3-030-30391-4_7