RT info:eu-repo/semantics/article
T1 Multi-rate vibration signal analysis for bearing fault detection in induction machines using supervised learning classifiers
A1 El Bouharrouti, Nada
A1 Moríñigo Sotelo, Daniel
A1 Belahcen, Anouar
K1 Machinery - Monitoring
K1 Sistema de Monitoreo
K1 Sampling (Statistics)
K1 Mechanical engineering
K1 Electrical engineering
K1 Machine learning
K1 Aprendizaje automático
K1 Vibration
K1 1209.10 Teoría y Técnicas de Muestreo
K1 3313 Tecnología E Ingeniería Mecánicas
K1 3306 Ingeniería y Tecnología Eléctricas
K1 2201.11 Vibraciones
AB Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency.
PB MDPI
SN 2075-1702
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/67988
UL https://uvadoc.uva.es/handle/10324/67988
LA eng
NO Machines, 2024, Vol. 12, Nº. 1, 17
NO Producción Científica
DS UVaDOC
RD 14-oct-2024