11 Improving Bearing Diagnostic Performance …
125
11.4 Conclusions
This study proposed the new feature-selection scheme that combines the GA-based
feature analysis and the k-NN classifier. This scheme is integrated into the model
for diagnosing bearing faults under variable operating conditions. The new featureselection scheme proved its outstanding performance via experimental results. More
specifically, the proposed scheme’s output, which is the optimal feature-set, helps
the classifier model achieve the classification accuracies of 98.33% and 97.22% in
terms of dataset 1 and dataset 2, respectively. much higher than the figures of the two
state-of-the-art rivals.
Acknowledgements This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the
Republic of Korea (Nos. 20162220100050, 20161120100350, 20172510102130). It was also funded
in part by The Leading Human Resource Training Program of Regional Neo industry through
the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and
future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program
through the National Research Foundation of Korea (NRF) funded by the Ministry of Education
(2016R1D1A3B03931927).
References
1. V. Tra, J. Kim, S.A. Khan, J.-M. Kim, Bearing fault diagnosis under variable speed using convolutional neural networks and the stochastic diagonal levenberg-marquardt algorithm. Sensors
17(12), 2834 (2017)
2. V. Tra, J. Kim, S.A. Khan, J.-M. Kim, Incipient fault diagnosis in bearings under variable speed
conditions using multiresolution analysis and a weighted committee machine, J. Acoust. Soc.
Am. 142(1), EL35–EL41 (2017)
3. M. Zhao, X. Jin, Z. Zhang, B. Li, Fault diagnosis of rolling element bearings via discriminative
subspace learning: visualization and classification. Expert Syst. Appl. 41(7), 3391–3401 (2014)
4. X. Jin, M. Zhao, T.W. Chow, M. Pecht, Motor bearing fault diagnosis using trace ratio linear
discriminant analysis. IEEE Trans. Industr. Electron. 61(5), 2441–2451 (2013)
5. V.H. Nguyen, J.-C. Golinval, Fault detection based on kernel principal component analysis. Eng.
Struct. 32(11), 3683–3691 (2010)
6. X. Wu et al., Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
7. R. Yan, R.X. Gao, X. Chen, Wavelets for fault diagnosis of rotary machines: a review with
applications. Sig. Process. 96, 1–15 (2014)
125
11.4 Conclusions
This study proposed the new feature-selection scheme that combines the GA-based
feature analysis and the k-NN classifier. This scheme is integrated into the model
for diagnosing bearing faults under variable operating conditions. The new featureselection scheme proved its outstanding performance via experimental results. More
specifically, the proposed scheme’s output, which is the optimal feature-set, helps
the classifier model achieve the classification accuracies of 98.33% and 97.22% in
terms of dataset 1 and dataset 2, respectively. much higher than the figures of the two
state-of-the-art rivals.
Acknowledgements This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the
Republic of Korea (Nos. 20162220100050, 20161120100350, 20172510102130). It was also funded
in part by The Leading Human Resource Training Program of Regional Neo industry through
the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and
future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program
through the National Research Foundation of Korea (NRF) funded by the Ministry of Education
(2016R1D1A3B03931927).
References
1. V. Tra, J. Kim, S.A. Khan, J.-M. Kim, Bearing fault diagnosis under variable speed using convolutional neural networks and the stochastic diagonal levenberg-marquardt algorithm. Sensors
17(12), 2834 (2017)
2. V. Tra, J. Kim, S.A. Khan, J.-M. Kim, Incipient fault diagnosis in bearings under variable speed
conditions using multiresolution analysis and a weighted committee machine, J. Acoust. Soc.
Am. 142(1), EL35–EL41 (2017)
3. M. Zhao, X. Jin, Z. Zhang, B. Li, Fault diagnosis of rolling element bearings via discriminative
subspace learning: visualization and classification. Expert Syst. Appl. 41(7), 3391–3401 (2014)
4. X. Jin, M. Zhao, T.W. Chow, M. Pecht, Motor bearing fault diagnosis using trace ratio linear
discriminant analysis. IEEE Trans. Industr. Electron. 61(5), 2441–2451 (2013)
5. V.H. Nguyen, J.-C. Golinval, Fault detection based on kernel principal component analysis. Eng.
Struct. 32(11), 3683–3691 (2010)
6. X. Wu et al., Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
7. R. Yan, R.X. Gao, X. Chen, Wavelets for fault diagnosis of rotary machines: a review with
applications. Sig. Process. 96, 1–15 (2014)