Transfer learning for remaining useful life prediction based on consensus self-organizing models
Yuantao Fan,
Nowaczyk, SÅ‚awomir and
Rögnvaldsson, Thorsteinn
Reliability Engineering and System Safety, 2020, vol. 203, issue C
Abstract:
The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold.
Keywords: Transfer learning; Feature-Representation transfer; Domain adaptation; Remaining useful life prediction; Consensus self-organizing models (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (16)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832020305998
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020305998
DOI: 10.1016/j.ress.2020.107098
Access Statistics for this article
Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares
More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).