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Authors = Quentin Sean Koplin

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21 pages, 6847 KiB  
Article
Individual Feature Selection of Rolling Bearing Impedance Signals for Early Failure Detection
by Florian Michael Becker-Dombrowsky, Quentin Sean Koplin and Eckhard Kirchner
Lubricants 2023, 11(7), 304; https://doi.org/10.3390/lubricants11070304 - 20 Jul 2023
Cited by 9 | Viewed by 2341
Abstract
Condition monitoring of technical systems has increasing importance for the reduction of downtimes based on unplanned breakdowns. Rolling bearings are a central component of machines because they often support energy-transmitting elements like shafts and spur gears. Bearing damages lead to a high number [...] Read more.
Condition monitoring of technical systems has increasing importance for the reduction of downtimes based on unplanned breakdowns. Rolling bearings are a central component of machines because they often support energy-transmitting elements like shafts and spur gears. Bearing damages lead to a high number of machine breakdowns; thus, observing these has the potential to reduce unplanned downtimes. The observation of bearings is challenging since their behavior in operation cannot be investigated directly. A common solution for this task is the measurement of vibration or component temperature, which is able to show an already occurred bearing damage. Measuring the electrical bearing impedance in situ has the ability to gather information about bearing revolution speed and bearing loads. Additionally, measuring the impedance allows for the detection and localization of damages in the bearing, as early research has shown. In this paper, the impedance signal of five fatigue tests is investigated using individual feature selection. Additionally, the feature behavior is analyzed and explained. It is shown that the three different bearing operational time phases can be distinguished via the analysis of impedance signal features. Furthermore, some of the features show a significant change in behavior prior to the occurrence of initial damages before the vibration signals of the test rig vary from a normal state. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
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