A conference (paper) presentation, presented at ECCE 2019.

1. Fault Diagnosis of Induction Motor Bearing Using Cepstrum-
based Preprocessing and Ensemble Learning Algorithm
[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
Presenter
Niloy Sikder

2. Feb 07, 2019 ECCE 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. 2
Methodology
Fig. 4: Block diagram of the proposed GB based fault analysis model
Feb 07, 2019 ECCE 2019

4. 3
Data Collection & Dataset Construction
Fig. 5: Test rig used by CWRU for collecting motor fault data[4]
Load 0 – 3 hp
Fault Seeder EDM
Diameter 0.007-0.021 inch
Collection rate 12000, 48000
samples/sec
rpm 1730, 1750, 1772, 1797
Table I: Available datasets in CWRU Lab website[5]
Load 3 hp
Number of fault classes 4
Diameter 0.014 inch
Collection rate 48000 samples/sec
rpm 1730
Fault Type Drive end type
Table II: Datasets used in this study
Feb 07, 2019 ECCE 2019

5. 3
Data Collection & Dataset Construction
Fig. 6: Data arrangement process
Feb 07, 2019 ECCE 2019

6. 4
Fig. 7: Cepstrum analysis algorithm
Feb 07, 2019 ECCE 2019
Data Preprocessing Using Real Cepstrum Analysis
 The IDFT of the logarithm of the absolute value of the DFT of the
input signal
Quefrency Frequency
Rahmonic Harmonic
Lifter Filter
Gamnitude Magnitude
Saphe Phase
Darius Radius
Dedomulation Demodulation
Table I: Terms coined by Bogert et. al.

7. 6
Data Preprocessing Using Real Cepstrum Analysis (cont.)
Fig. 8: Raw HBC signal in (a) time-domain, and (b) quefrency-domain
Feb 07, 2019 ECCE 2019

8. 7
Data Normalization
Feb 07, 2019 ECCE 2019

9. 8
Data Normalization (cont.)
𝑥 𝑛𝑜𝑟𝑚 =
𝑥 − 𝑥 𝑚𝑖𝑛
𝑥 𝑚𝑎𝑥 − 𝑥 𝑚𝑖𝑛
𝑥 𝑛𝑜𝑟𝑚 =
𝑥 − 𝑥 𝑚𝑒𝑎𝑛
𝑠𝑡𝑑(𝑥)
Fig. 9: Preprocessed signal (a) original, and (b) normalized
Feb 07, 2019 ECCE 2019

10.  Supervised learning algorithm
GradientBoosting Classifier
9Feb 07, 2019 ECCE 2019
 Incorporates decision trees
 Ensemble algorithm
 Boosting technique
 Useful for classification and regression problems

11. 10
Decision Tree
Fig. 10: An imaginary decision tree of Jack’s destination
𝐴𝑚𝑒𝑟𝑖𝑐𝑎, 𝐸𝑛𝑔𝑙𝑎𝑛𝑑, 𝐼𝑛𝑑𝑖𝑎, 𝐹𝑟𝑎𝑛𝑐𝑒, 𝐺𝑒𝑟𝑚𝑎𝑛𝑦
Feb 07, 2019 ECCE 2019
𝑷𝒓𝒐𝒃𝒍𝒆𝒎𝒔: 𝑵𝒐𝒊𝒔𝒆, 𝑽𝒂𝒓𝒊𝒂𝒏𝒄𝒆 & 𝑩𝒊𝒂𝒔

12. 11
Fig. 11: Multiple decision trees from multiple friends
America 3
England 2
India 2
France 2
Germany 1
Table IV: Votes received by each country
Feb 07, 2019 ECCE 2019
Bagging Process

13. Boosting Process
Fig. 12: Boosting process
12Feb 07, 2019 ECCE 2019

14. 13
Experimental Results
Fig. 13: Accuracy as a function of the train size Fig. 14: Accuracy as a function of the number of estimators
Feb 07, 2019 ECCE 2019

15. 14
Experimental Results (cont.)
Fig. 15: The accuracy of GB classifier as a function of the
learning rate and maximum depth
Feb 07, 2019 ECCE 2019
Parameter/Attribute
name
Value
Train size 0.75
Number of estimators 100
Learning Rate 0.7, 0.8
Maximum depth 2
Subsample 1
Table V: GB classifier parameter values for maximum
accuracy
Accuracy = 99.58%

16. 15
Experimental Results (cont.)
Fig. 17: GB classifier AUC-ROCFig. 16: Confusion matrix for each bearing condition
Feb 07, 2019 ECCE 2019

17. 16
Comparison with Previous Methods
Feb 07, 2019 ECCE 2019
Methodology
Average sensitivity of each
fault class (%) ACA
(%)HBC IRF BRF ORF
Proposed 99.85 99.2 99.55 99.65 99.58
[8] 92 88 87 83 87.25
[9] 88 70 83 87 82
[10] 99.44 98.54 98.7 99.2 98.97
Metric Precision
(%)
Recall
(%)
F1-score
(%)
Accuracy
(%)
Proposed 100 100 100 99.58
[9] 94.52 93.6 93.6 94.06
[10] 100 100 100 98.97

18. 18
Future Developments
Feb 07, 2019 ECCE 2019
 Real time fault classification
 Improve in terms of complexity, processing time and hardware
requirements

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

20. 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/
[8] R. Islam, S. A. Khan, and J.-M. Kim, “Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in
Induction Motors,” Journal of Sensors, vol. 2016, pp. 1–16, 2016.
[9] O. Janssens, V. Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, R. V. D. Walle, and S. V. Hoecke, “Convolutional Neural Network Based
Fault Detection for Rotating Machinery,” Journal of Sound and Vibration, vol. 377, pp. 331–345, 2016.
[10] N. Sikder, K. Bhakta, A. Nahid and M. M. M. Islam, “Fault Diagnosis of Motor Bearing Using Ensemble Learning Algorithm with FFT-based
Preprocessing.” In-press.