Feb. 9, 2024, 5:44 a.m. | Mohammad Al-Sa'd Tuomas Jalonen Serkan Kiranyaz Moncef Gabbouj

cs.LG updates on arXiv.org arxiv.org

Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic conditions such as time-varying rotational speeds and the vibration's non-stationary nature. This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under time-varying speeds and varying noise levels. First, we formulate the …

analysis contributors costs cs.ai cs.lg cs.sy diagnosis eess.sy environments health insights machine maintenance signal

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

ETL Developer

@ Gainwell Technologies | Bengaluru, KA, IN, 560100

Medical Radiation Technologist, Breast Imaging

@ University Health Network | Toronto, ON, Canada

Data Scientist

@ PayPal | USA - Texas - Austin - Corp - Alterra Pkwy