Presented as a conference (paper) presentation (ICREST 2019). Doi: 10.1109/ICREST.2019.8644089

1. Fault Diagnosis of Motor Bearing Using Ensemble Learning
Algorithm with FFT-based Preprocessing
[Conference Presentation]
Contributing Authors
Niloy Sikder
M.Sc. Student
CSE Discipline
Khulna University, Khulna
niloysikder333@gmail.com
Kangkan Bhakta
M.Sc. Student
ECE Discipline
Khulna University, Khulna
kangkanbhakta@gmail.com
Dr. Abdullah Al Nahid
Associate Professor
ECE Discipline
Khulna University, Khulna
nahid.ece.ku@gmail.com
M M Manjurul Islam
PhD Student
School of Electrical,
Electronics and Computer
Engineering
University of Ulsan, Ulsan,
Republic of Korea
m.m.manjurul@gmail.com

2. Jan 10, 2019 ICREST 2019 1
Electric Motor
Fig. 1: Cross-section of an electric motor[1]
Fig. 2: Rotating motor bearings[2]
Fig. 3: Industrial motors[3]

3. Jan 10, 2019 ICREST 2019 2
The Methodology
Fig. 4: Block diagram of the proposed RF based fault analysis model

4. Jan 10, 2019 ICREST 2019 3
Data Collection & Dataset Construction
Fig. 5: Test rig used by CWRU for collecting motor fault data[4]
Load 2hp
Fault Seeder EDM
Diameter 0.007-0.04 inch
Collection rate 12000, 48000
samples/sec
rmp 1720-1797
Table I: Available datasets in CWRU Lab website[5]
Load 2hp
Number of fault classes 4
Diameter 0.014 inch
Collection rate 48000 samples/sec
rmp 1730
Table II: Datasets used in this study
Chart I: Dataset organization

5. Jan 10, 2019 ICREST 2019 4
Data Preprocessing Using Fourier Transform
Fig. 6: Outline of the face of a cat
Fig. 6: Outline of the face of a cat Fig. 7: Sinusoidal waves used to draw the cat face

6. Jan 10, 2019 ICREST 2019 5
Data Preprocessing Using Fourier Transform (cont.)
Fig. 8: Summation of the sinusoids
Fig. 9: Frequency-domain representation of the previous
signal (partial)

7. Jan 10, 2019 ICREST 2019 6
Data Preprocessing Using Fourier Transform (cont.)
Fig. 10: Raw HBC signal in (a) time-domain, and (b) frequency-domain

8. Jan 10, 2019 ICREST 2019 7
Data Normalization

9. Jan 10, 2019 ICREST 2019 8
Data Normalization (cont.)
𝑥 𝑛𝑜𝑟𝑚 =
𝑥 − 𝑥 𝑚𝑖𝑛
𝑥 𝑚𝑎𝑥 − 𝑥 𝑚𝑖𝑛
𝑥 𝑛𝑜𝑟𝑚 =
𝑥 − 𝑥 𝑚𝑒𝑎𝑛
𝑠𝑡𝑑(𝑥)
Fig. 11: Raw HBC signal in (a) original, and (b) normalized

10.  Ensemble
 Decision Trees
 Supervision
Random Forest Classifier
Fig. 12: Randomly organized trees in a forest[6]
Jan 10, 2019 ICREST 2019 9

11. Jan 10, 2019 ICREST 2019 10
Decision Tree
Fig. 13: An imaginary decision tree of Jack’s destination
𝐴𝑚𝑒𝑟𝑖𝑐𝑎, 𝐸𝑛𝑔𝑙𝑎𝑛𝑑, 𝐼𝑛𝑑𝑖𝑎, 𝐹𝑟𝑎𝑛𝑐𝑒, 𝐺𝑒𝑟𝑚𝑎𝑛𝑦

12. Jan 10, 2019 ICREST 2019 11
From Decision Trees to Random Forest
Fig. 14: Multiple decision trees from multiple friends
America 3
England 2
India 2
France 2
Germany 1
Table III: Votes received by each country

13. Jan 10, 2019 ICREST 2019 12
Experimental Outputs
Fig. 15: Accuracy as a function of the test size Fig. 16: Accuracy as a function of the number of estimators
Accuracy = 98.97%

14. Jan 10, 2019 ICREST 2019 13
Experimental Outputs (cont.)
Fig. 18: RF classifier AUC-ROCFig. 17: Confusion matrix for each bearing condition

15. March 27, 2017 ECE Discipline, KU 35
THANK YOU
ANY QUESTIONS?

16. References
[1] “Why electric motors fail,” Flow Control Network, 06-Apr-2018. [Online]. Available: https://www.flowcontrolnetwork.com/why-electric-motors-fail/.
[Accessed: 08-Jan-2019].
[2] “Bearing (mechanical),” Wikipedia, 03-Dec-2018. [Online]. Available: https://en.wikipedia.org/wiki/Bearing_(mechanical). [Accessed: 08-Jan-2019].
[3] [Online]. https://www.topsimages.com/. [Accessed: 08-Jan-2019].
[4] W. A. Smith and R. B. Randall, “Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study,” Mechanical
Systems and Signal Processing, vol. 64-65, pp. 100–131, 2015.
[5] Bearing Data Center. [Online]. Available: https://csegroups.case.edu/. [Accessed: 08-Jan-2019].
[6] N. Donges, “The Random Forest Algorithm – Towards Data Science,” Towards Data Science, 22-Feb-2018. [Online]. Available:
https://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd. [Accessed: 09-Jan-2019].
[7] Icons collected from: https://www.iconfinder.com/