Journal cover Journal topic
Journal of Sensors and Sensor Systems An open-access peer-reviewed journal
Journal topic

Journal metrics

CiteScore value: 2.9
CiteScore
2.9
SNIP value: 1.056
SNIP1.056
IPP value: 1.38
IPP1.38
SJR value: 0.361
SJR0.361
Scimago H <br class='widget-line-break'>index value: 13
Scimago H
index
13
h5-index value: 13
h5-index13
Download
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.
JSSS | 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.

Viewed

Total article views: 887 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
708 155 24 887 20 16
  • HTML: 708
  • PDF: 155
  • XML: 24
  • Total: 887
  • BibTeX: 20
  • EndNote: 16
Views and downloads (calculated since 12 May 2020)
Cumulative views and downloads (calculated since 12 May 2020)

Viewed (geographical distribution)

Total article views: 751 (including HTML, PDF, and XML) Thereof 693 with geography defined and 58 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 15 May 2021

Publications Copernicus
Download
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.
Citation