Articles | Volume 9, issue 1
J. Sens. Sens. Syst., 9, 143–155, 2020
https://doi.org/10.5194/jsss-9-143-2020

Special issue: Sensors and Measurement Systems 2019

J. Sens. Sens. Syst., 9, 143–155, 2020
https://doi.org/10.5194/jsss-9-143-2020

Regular research article 12 May 2020

Regular research article | 12 May 2020

Data-driven vibration-based bearing fault diagnosis using non-steady-state training data

Kurt Pichler et al.

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Cited articles

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Albarbar, A., Mekid, S., Starr, A., and Pietruszkiewicz, R.: Suitability of MEMS accelerometers for condition monitoring: An experimental study, Sensors, 8, 784–799, 2008. a
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Short summary
Detecting faults in bearings is indispensable for the maintenance of many industrial machines or components. The faults are detected by recognizing patterns in vibration data that are measured at the bearing housing. The method can be trained with data of variable revolution speeds, therefore reducing the effort for the acquisition of training data. Moreover, incipient faults can be detected before they cause severe damage to the equipment.