Automating Predictive Maintenance for Energy Efficiency via Machine Learning and IoT Sensors

10 pages•Published: November 2, 2021

Paul Bodily, Isaac Griffith, Mary Hofle, Omid Heidari, Safal Lama, Avery Conlin, Andrew Christiansen, Delaney Moore, Kellie Wilson, Anish Sebastian and Marco Schoen

Abstract

The arise of maintenance issues in mechanical systems is cause for decreased energy efficiency and higher operating costs for many small- to medium-sized businesses. The sooner such issues can be identified and addressed, the greater the energy savings. We have designed and implemented an automated predictive maintenance system that uses machine learning models to predict maintenance needs from data collected via data sensors attached to mechanical systems. As a proof of concept, we demonstrate the effectiveness of the system by predicting several operating states for a standard clothes dryer.

Keyphrases: internet of things, machine learning, mechanical systems, predictive maintenance

In: Yan Shi, Gongzhu Hu, Quan Yuan and Takaaki Goto (editors). Proceedings of ISCA 34th International Conference on Computer Applications in Industry and Engineering, vol 79, pages 54-63.

Links:	https://easychair.org/publications/paper/vb4g
	https://doi.org/10.29007/rw47

BibTeX entry

@inproceedings{CAINE2021:Automating_Predictive_Maintenance_Energy,
  author    = {Paul Bodily and Isaac Griffith and Mary Hofle and Omid Heidari and Safal Lama and Avery Conlin and Andrew Christiansen and Delaney Moore and Kellie Wilson and Anish Sebastian and Marco Schoen},
  title     = {Automating Predictive Maintenance for Energy Efficiency via Machine Learning and IoT Sensors},
  booktitle = {Proceedings of ISCA 34th International Conference on Computer Applications in Industry and Engineering},
  editor    = {Yan Shi and Gongzhu Hu and Quan Yuan and Takaaki Goto},
  series    = {EPiC Series in Computing},
  volume    = {79},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/vb4g},
  doi       = {10.29007/rw47},
  pages     = {54-63},
  year      = {2021}}

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