Bearing fault diagnosis using machine learning and a novel set of fault-related spectral features

• João Paulo Vieira UFSC
• Rodrigo Kobashikawa Rosa UFSC
• Victor Afonso Bauler UFSC
• Danilo Braga Dynamox
• Danilo Silva UFSC

DOI: https://doi.org/10.5753/eniac.2024.245033

Abstract

This article develops methods based on machine learning for fault diagnosis in bearings using data from Case Western Reserve University (CWRU). A multi-label approach proposed in the literature is adopted, with three binary classifiers to identify faults in the inner race, outer race, and rolling element, dividing the dataset by distinct bearings to prevent leakage. Features in the time and frequency domains are used, including a new proposal of spectral features called PFRs (Peak-To-Floor Ratios), calculated over frequencies related to the fault phenomenon. The results, compared with other features from the literature, show improvements in performance and explainability.

Keywords: Bearing fault, Data Leakage, Spectral features

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