3D Multibody Simulation of Realistic Rolling Bearing Defects for Fault Classifier Development

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Rolling bearing faults stand out as the most prevalent type of fault in electrical machines. In this study, we leveraged geometry-based 3D multibody simulation to facilitate data-driven fault diagnosis. A comprehensive dataset was generated, encompassing data from both healthy and faulty bearings with realistic outer ring and inner ring faults of different types and sizes, operating at varying rotational speeds. Spectral analyses of the simulated bearing shaft displacement data proved that the bearing faults consistently appear at expected characteristic fault frequencies, with peak amplitudes correlating to the given fault size and rotation speed. Using the simulated data, we evaluated numerous feature engineering methods for machine learning-based fault classification. The classification results demonstrated a successful differentiation of simulated faults, whether on the outer ring or inner ring, from the healthy counterparts.
Original languageEnglish
Title of host publicationProceedings of ICEM2024
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages7
Publication statusAccepted/In press - Oct 2024
MoE publication typeA4 Article in a conference publication
Event26th International Conference on Electrical Machines, ICEM 2024 - Politecnico di Torino, Turin, Italy
Duration: 1 Sept 20244 Sept 2024
https://www.symposium.it/en/events/2024/26th-international-conference-on-electrical-machines-icem-2024

Conference

Conference26th International Conference on Electrical Machines, ICEM 2024
Country/TerritoryItaly
CityTurin
Period1/09/244/09/24
Internet address

Funding

This work received funding from the Research Council of Finland through two projects: ’Intelligent Techniques in Condition Monitoring of Electromechanical Energy Conversion Systems’ (ESTV) and ’The Centre of Excellence in High-Speed Electromechanical Energy Conversion Systems’ (HiECSs) with the decision numbers 331199 and 346441, respectively.

Keywords

  • multibody simulation
  • machine learning
  • rolling bearing
  • fault classification
  • simulated data

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