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

Milla Vehvilainen, Mikko Tahkola, Janne Keranen, Nada El Bouharrouti, Pekka Rahkola, Jari Halme, Jenni Pippuri-Makelainen, Anouar Belahcen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

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.

AlkuperäiskieliEnglanti
Otsikko2024 International Conference on Electrical Machines, ICEM 2024
KustantajaIEEE
Sivumäärä7
ISBN (elektroninen)979-8-3503-7060-7
DOI - pysyväislinkit
TilaJulkaistu - 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Electrical Machines - Politecnico di Torino, Turin, Italia
Kesto: 1 syysk. 20244 syysk. 2024

Julkaisusarja

NimiProceedings (International Conference on Electrical Machines)
ISSN (elektroninen)2473-2087

Conference

ConferenceInternational Conference on Electrical Machines
LyhennettäICEM
Maa/AlueItalia
KaupunkiTurin
Ajanjakso01/09/202404/09/2024

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