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

doi:10.1016/j.cirp.2019.04.104

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 journal › Article › peer-review

44 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 language	English (US)
Pages (from-to)	459-462
Number of pages	4
Journal	CIRP Annals
Volume	68
Issue number	1
DOIs	https://doi.org/10.1016/j.cirp.2019.04.104
State	Published - 2019

All Science Journal Classification (ASJC) codes

Mechanical Engineering
Industrial and Manufacturing Engineering

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10.1016/j.cirp.2019.04.104

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title = "Machine learning and AI for long-term fault prognosis in complex manufacturing systems",

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.",

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AU - Dave, Darpit

AU - Kumara, Soundar R.T.

PY - 2019

Y1 - 2019

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AB - 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.

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

Abstract

All Science Journal Classification (ASJC) codes

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