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

Jukka Junttila (Corresponding author), Ville Lämsä, Leonardo Espinosa-Leal, Anssi Sillanpaa

Research output: Contribution to conferenceConference PosterScientific

Abstract

Data-based models for operational state recognition and
detection of abnormal operation of a gas engine generating
set (genset) in near real-time were provided. One model can
classify the current power output level very accurately, and
the other can detect abnormal operation (novelties), e.g., in
fault situations, at a specific load level. Thus, a fast and
accurate two-step state recognition model can be built.
Original languageEnglish
Number of pages1
DOIs
Publication statusPublished - 21 Mar 2023
MoE publication typeNot Eligible
EventFCAI AI Day 2022 - Dipoli, Aalto University, Espoo, Finland
Duration: 16 Nov 202216 Nov 2022
https://fcai.fi/ai-day-2022

Conference

ConferenceFCAI AI Day 2022
Country/TerritoryFinland
CityEspoo
Period16/11/2216/11/22
Internet address

Keywords

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

Fingerprint

Dive into the research topics of 'Feature engineering –based machine learning models for operational state recognition of rotating machines'. Together they form a unique fingerprint.

Cite this