Data-based energy performance root cause analysis methodology

Robin Zink*, Dominik Flick, Christoph Hermann, Sebastian Thiede, Matthias Weigold

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

10 Downloads (Pure)

Abstract

The automotive industry aims to achieve continual improvement in energy performance to reduce emissions and costs. High complexity in manufacturing systems makes comprehensive analyses economically infeasible thus a root cause analysis (RCA) methodology is required to identify and quantify subsystems with a high energy saving potential. This allows targeted analyses and measures to be derived. For this purpose, a data-based benchmarking approach for identification and quantification of energy saving potential is developed. The methodology has proven to be suitable to derive significant energy savings in the validation based on a real use case of a globally operating car manufacturer.

Original languageEnglish
Pages (from-to)1037-1042
Number of pages6
JournalProcedia CIRP
Volume130
Early online date27 Nov 2024
DOIs
Publication statusPublished - Dec 2024
Event18th IFAC Workshop on Time Delay Systems, TDS 2024 - Udine, Italy
Duration: 2 Oct 20235 Oct 2023
Conference number: 18

Keywords

  • artificial intelligence
  • energy efficiency
  • factory
  • machine learning
  • manufacturing

Fingerprint

Dive into the research topics of 'Data-based energy performance root cause analysis methodology'. Together they form a unique fingerprint.

Cite this