Machine learning and AI for long-term fault prognosis in complex manufacturing systems

Satish T.S. Bukkapatnam, Kahkashan Afrin, Darpit Dave, Soundar R.T. Kumara

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Recent advances in sensors and other streaming data sources of plant floor automation and information systems open an exciting possibility to predict the risks of faults and breakdowns across a manufacturing plant over much longer time horizons than what is conceivable today. This paper introduces a Manufacturing System-wide Balanced Random Survival Forest (MBRSF), a nonparametric machine learning approach that can fuse complex dynamic dependencies underlying these data streams to provide a long-term prognosis of machine breakdowns. Experimental investigations with a 20 machine automotive manufacturing line suggest that MBRSF reduces prediction errors (Brier scores) by over 90% compared to other methods tested.

Original languageEnglish (US)
Pages (from-to)459-462
Number of pages4
JournalCIRP Annals
Volume68
Issue number1
DOIs
StatePublished - 2019

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All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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