BibTeX Export for Data-Driven RUL Prediction Using Performance Metrics (Short Paper)

Copy to Clipboard Download

@InProceedings{diazgonzalez_et_al:OASIcs.DX.2024.21,
  author =	{Diaz-Gonzalez, Abel and Coursey, Austin and Quinones-Grueiro, Marcos and Kulkarni, Chetan S. and Biswas, Gautam},
  title =	{{Data-Driven RUL Prediction Using Performance Metrics}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{21:1--21:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.21},
  URN =		{urn:nbn:de:0030-drops-221135},
  doi =		{10.4230/OASIcs.DX.2024.21},
  annote =	{Keywords: remaining useful life, data-driven methods, machine learning, performance metric, multitask machine learning, Monte Carlo}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode