IEEE Computational Intelligence Magazine - August 2021 - 93

of high-level features from three different
views of features. A new fitness function
based on the measures of accuracy and
distance was developed to enable these
newly constructed features to be accurate
and discriminative. To improve the
diagnosis performance, an ensemble classifier
was created by using constructed
features from multiple views and using
KNN. With these designs, MFCGPE can
not only automatically select and construct
informative and discriminative features
from different views but also build
an effective ensemble to achieve a high
generalization performance.
The effectiveness of MFCGPE was
evaluated on three rolling bearing fault
datasets and compared with the 19 competitive
methods. The results showed that
MFCGPE achieved the best diagnosis
accuracy on the three datasets among all
the methods. The highlight of MFCGPE
was that multiple discriminative features
were automatically constructed using
MFCGPE, and the ensemble diagnosis
can address the issue of poor generalization
of the diagnosis model caused by
using a small number of training samples.
This paper shows that the proposed
MFCGPE approach is effective for fault
diagnosis of rolling bearings. In addition
to fault diagnosis, remaining life prediction
can also help to analyze rolling
bearing degradation. In the future, we
will investigate how GP is used to construct
the health index to predict the
remaining life of the rolling bearing.
Acknowledgments
This work was supported in part by the
National Natural Science Foundation of
China under grant 51777075, the Natural
Science Foundation of Hebei Province
under grant E2019502064, the
Marsden Fund of New Zealand Government
under Contracts VUW1509 and
VUW1615, the Science for Technological
Innovation Challenge (SfTI) fund
under grant E3603/2903, the University
Research Fund at Victoria University of
Wellington grant number 223805/3986,
MBIE Data Science SSIF Fund under
the contract RTVU1914, and National
Natural Science Foundation of China
(NSFC) under Grant 61876169.
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AUGUST 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 93
IEEE Computational Intelligence Magazine - August 2021

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