Please use this identifier to cite or link to this item: doi:10.22028/D291-37191
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Title: Data and computation efficient model-based fault detection for rolling element bearings using numerical differentiation
Author(s): Othmane, Amine
Rudolph, Joachim
Language: English
Title: 5th Conference on Control and Fault Tolerant Systems : September 29th-October 1st, 2021, Congress Center, St-Raphaël, France
Startpage: 163
Endpage: 168
Publisher/Platform: IEEE
Year of Publication: 2021
Place of publication: Piscataway
Place of the conference: Saint-Raphael, France
Free key words: Vibrations
Fault diagnosis
Fault tolerance
Computational modeling
Fault detection
Fault tolerant systems
DDC notations: 600 Technology
Publikation type: Conference Paper
Abstract: A novel model-based approach for online condition monitoring of rolling element bearings based on efficient and robust numerical differentiation is proposed. The physical parameters may be identified using fault-free data. The main idea for fault detection is the design of signals, called residuals, sensitive only to faults using a simplified model of the bearing vibrations. These residuals are proportional to the force stemming from the faults and are easy to compute and implement. An approach for the design of a failure detection threshold is provided. This threshold can be designed to meet a desired probability of false alarms. The method is validated using four experimental data sets stemming from different research centers.
DOI of the first publication: 10.1109/SysTol52990.2021.9595974
URL of the first publication:
Link to this record: urn:nbn:de:bsz:291--ds-371914
ISBN: 978-1-6654-3159-0
Date of registration: 13-Sep-2022
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Joachim Rudolph
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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