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Transfer learning for remaining useful life prediction based on consensus self-organizing models

Yuantao Fan, Nowaczyk, Sławomir 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
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020305998

DOI: 10.1016/j.ress.2020.107098

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