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Feature engineering –based machine learning models for operational state recognition of rotating machines

PUBLISHED March 21, 2023 (DOI: https://doi.org/10.54985/peeref.2303p8483224)

NOT PEER REVIEWED

Authors

Jukka Junttila1 , Ville Lämsä1 , Leonardo Espinosa Leal2 , Anssi Sillanpää3
  1. VTT Technical Research Centre of Finland
  2. Arcada University of Applied Sciences
  3. Wärtsilä Finland Oy

Conference / event

AI DAY 2022, November 2023 (Espoo, Finland)

Poster summary

This poster sums up the work done in a master’s thesis and two subsequent publications. Our aim is to provide data-based models for operational state recognition and detection of abnormal operation of a gas engine generating set in near real-time. Our idea is to use computationally light features extracted from measured mechanical vibration data that are sensitive to the changes in the operational state to build classification and novelty detection models. The classifier model groups different states of normal operation, different power output levels in our case, and the novelty detector can detect abnormal operation at a specific power output level. As a result, we managed to build fast and accurate two-step state recognition model by combining the classification and novelty detection models. We also studied simulated (FEM) mechanical vibration responses. A comparative validation showed that the simulated responses resemble the measured ones statistically but revealed significant absolute differences.

Keywords

Operational state recognition, Feature engineering, Internal combustion engine, Mechanical vibration, Simulation, Classification

Research areas

Mechanical Engineering, Computer and Information Science

References

No data provided

Funding

No data provided

Supplemental files

  1. Masters thesis related to poster   Download

Additional information

Competing interests
No competing interests were disclosed.
Data availability statement
Data sharing not applicable to this poster as no datasets were generated or analyzed during the current study.
Creative Commons license
Copyright © 2023 Junttila et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Cite
Junttila, J., Lämsä, V., Espinosa Leal, L., Sillanpää, A. Feature engineering –based machine learning models for operational state recognition of rotating machines [not peer reviewed]. Peeref 2023 (poster).
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