Product quality driven auto-prognostics: low-cost digital solution for SMEs

Amit Kumar Jain*, Maharshi Dhada, Ajith Kumar Parlikad, Bhupesh Kumar Lad

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
24 Downloads (Pure)

Abstract

Setting out existing prognostics solutions in small and medium enterprises (SMEs) is accompanied by challenges. These include employing expensive sensors, acquisition systems; and attending geometric limitations. Additionally, these solutions call for a specialist to take on feature engineering, machine learning algorithm selection, etc. Presented in this paper is a low-cost digital solution (intelligently integrate cost-cutting off-the-shelf technologies) for SMEs via product quality driven auto-prognostics. First, we develop upon existing solutions by addressing their drawbacks viz. cost, geometric limitations via a new product quality-centered condition monitoring strategy. Every SME must investigate the quality of their products, and therefore the authors believe this to be a low-cost solution. Next, the proposed solution integrates automated machine learning via Auto-WEKA, an off-the-shelf open-source technology. Lastly, the practical advantages of the proposed solution over the existing sensor-based solution were investigated via a case study. Results depict that this low-cost prognostics solution is vital for maintenance planning in SMEs.
Original languageEnglish
Title of host publication4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies AMEST 2020
EditorsAjith Parlikad, Christos Emmanouilidis, Benoit Iung, Marco Macchi
PublisherInternational Federation of Automatic Control (IFAC)
Pages78-83
Number of pages6
DOIs
Publication statusPublished - 18 Dec 2020
Event4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies - Cambridge, United Kingdom
Duration: 10 Sep 202011 Sep 2020
https://www.amest2020.eng.cam.ac.uk/

Publication series

NameIFAC PapersOnline
PublisherInternational Federation of Automatic Control (IFAC)
ISSN (Print)2405-8963

Conference

Conference4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies
Abbreviated titleAMEST 2020
Country/TerritoryUnited Kingdom
CityCambridge
Period10/09/2011/09/20
Internet address

Keywords

  • automated machine learning
  • digital manufacturing
  • low-cost solutions
  • prognostics
  • quality

Fingerprint

Dive into the research topics of 'Product quality driven auto-prognostics: low-cost digital solution for SMEs'. Together they form a unique fingerprint.

Cite this