by Christian Lessmeier, James Kuria Kimotho, Detmar Zimmer, Walter Sextro
Abstract:
This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of datadriven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research
Reference:
Lessmeier, C.; Kimotho, J. K.; Zimmer, D.; Sextro, W.: Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification. European Conference of the Prognostics and Health Management Society, 2016.
Bibtex Entry:
@INPROCEEDINGS{Lessmeier2016,
author = {Christian Lessmeier AND James Kuria Kimotho AND Detmar Zimmer AND
Walter Sextro},
title = {Condition Monitoring of Bearing Damage in Electromechanical Drive
Systems by Using Motor Current Signals of Electric Motors: A Benchmark
Data Set for Data-Driven Classification},
booktitle = {European Conference of the Prognostics and Health Management Society},
year = {2016},
abstract = {This paper presents a benchmark data set for condition monitoring
of rolling bearings in combination with an extensive description
of the corresponding bearing damage, the data set generation by experiments
and results of datadriven classifications used as a diagnostic method.
The diagnostic method uses the motor current signal of an electromechanical
drive system for bearing diagnostic. The advantage of this approach
in general is that no additional sensors are required, as current
measurements can be performed in existing frequency inverters. This
will help to reduce the cost of future condition monitoring systems.
A particular novelty of the present approach is the monitoring of
damage in external bearings which are installed in the drive system
but outside the electric motor. Nevertheless, the motor current signal
is used as input for the detection of the damage. Moreover, a wide
distribution of bearing damage is considered for the benchmark data
set. The results of the classifications show that the motor current
signal can be used to identify and classify bearing damage within
the drive system. However, the classification accuracy is still low
compared to classifications based on vibration signals. Further,
dependency on properties of those bearing damage that were used for
the generation of training data are observed, because training with
data of artificially generated and real bearing damages lead to different
accuracies. Altogether a verified and systematically generated data
set is presented and published online for further research},
file = {Lessmeier2016.pdf:Lessmeier2016.pdf:PDF},
owner = {tobiasm},
timestamp = {2016.08.26},
url = {https://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2015/phmec_16_003.pdf}
}