Machine learning and AI for long-term fault prognosis in complex manufacturing systems. Issue 1 (2019)

• Record Type: Journal Article
• Authors: Bukkapatnam, Satish T.S.; Afrin, Kahkashan; Dave, Darpit; Kumara, Soundar R.T.
• Is Part Of: CIRP annals. Volume 68:Issue 1(2019)
• Page Start: 459
• Page End: 462
• Publication Date: 2019
• Subjects: Manufacturing system -- Artificial intelligence -- Performance; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2019.04.104
• Languages: English
• ISSNs: 0007-8506

Abstract

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.