Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

Comparison of Conventional Method of Fault Determination with Data-Driven Approach for Ball Bearings in a Wind Turbine Gearbox

Volume 14, Number 3, March 2018, pp. 397-412
DOI: 10.23940/ijpe.18.03.p1.397412

V. N. Balavignesh, B. Gundepudi, G. R. Sabareesh, and I. Vamsi

Department of Mechanical Engineering, BITS Pilani – Hyderabad campus, Jawahar Nagar, Shameerpet, Rangareddy District, 500078, India

(Submitted on June 24, 2017; Revised on December 26, 2017; Accepted on December 29, 2017)


Abstract:

The presented investigation on fault diagnosis of ball bearings compares the conventional method using FFT spectra with a data-driven approach using Support Vector Machines (SVMs). Three different cases of bearings (one healthy and two faulty bearings with different crack thickness) were used as experimental cases. The experimentally obtained time-domain acceleration signals were converted to the frequency-domain and de-noised using optimal wavelets selected based on relative magnitudes of Shannon entropy and energy values. The dominant peak was identified for each case and was subsequently compared with the characteristic bearing frequencies evaluated theoretically. The wavelet transformed time-domain experimental data was also used to train the SVM classifier. Also, the effect of statistical tools such as Principal Component Analysis (PCA) and Zero-phase Component Analysis (ZCA) on the classification accuracy of normal SVM and wavelet feature extraction-based SVM have been investigated.

 

References: 29

1.        S. Abbasion, A. Rafsanjani, A. Farshidianfar and N. Irani, "Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine," Mechanical Systems and Signal Processing, vol. 21, pp. 2933-2945, October 2007.

2.        S. Biswal, J. D. George and G.R. Sabareesh, "Fault Size Estimation Using Vibration Signatures in a Wind Turbine Test-rig," Procedia Engineering, vol. 144, pp. 305-311, May 2016.

3.        S. Biswal and G. R. Sabareesh, "Design and development of a wind turbine test rig for condition monitoring studies," International Conference on Industrial Instrumentation and Control (ICIC), College of Engineering Pune, India, May 2015.

4.        J. Chen, Z. Wan, J. Pan, Y. Zi, Y. Wang, B. Chen, H. Sun, J. Yuan and Z. He, "Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain," Mechanical Systems and Signal Processing, vol. 68-69, pp. 44-67, February 2016.

5.        X. Chen, J. Zhou, J. Xiao, X. Zhang, H. Xiao, W. Zhu and W. Fu, "Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings," Applied Mathematics and Computation, vol. 247, pp. 835-847, November 2014.

6.        H. D. M. de Azevedo, A. M. Arajo, and N. Bouchinneau, "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, vol. 56, pp. 368-379, April 2016.

7.        K. C. Gryllias and I. A Antoniadis, "A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments," Engineering Applications of Artificial Intelligence, vol. 25, pp. 326-344, March 2012.

8.        V. H. Jaramillo, J. R. Ottewill, R. Dudek, D. Lepiarczyk and P. Pawlik, "Condition monitoring of distributed systems using two-stage Bayesian inference data fusion," Mechanical Systems and Signal Processing, vol. 87, pp. 91-110, March 2017.

9.        P. K. Kankar, S.C. Sharma and S.P. Harsha, "Rolling element bearing fault diagnosis using wavelet transform," Neurocomputing, vol. 74, pp. 1638-1645, May 2011.

10.     E. Kannatey-Asibu, J. Yum and T. H. Kim, "Monitoring tool wear using classifier fusion," Mechanical Systems and Signal Processing, vol. 85, pp. 651-661, February 2017.

11.     C. Kar and A. R. Mohanty, "Monitoring gear vibrations through motor current sig-nature analysis and wavelet transform," Mechanical Systems and Signal Processing, vol. 20, pp. 158-187, January 2006.

12.     T. W. Lee, M. Girolami, A. J. Bell and T. Sejnowski, "A unifying information-theoretic framework for independent component analysis," Computers Mathematics with Applications, vol. 39, pp. 1-21, June 2000.

13.     H. Li, J. Zhao, X. Zhang and X. Ni, "A New Fault Diagnosis Method for Planetary Gearbox," International Journal of Performability Engineering, vol. 12, pp389-394, July 2016.

14.     C. Lin and V. Makis, "Application of vector time series modeling and T-squared control chart to detect early gearbox deterioration," International Journal of Performability Engineering, vol. 10, pp. 105-114, January 2014.

15.     C. U. Mba, H. A. Gabbar, S. Marchesiello, A. Fasana, and L. Garibaldi, "Fault Diagnosis in Flywheels: Case Study of a Reaction Wheel Dynamic System with Bearing Imperfections," International Journal of Performability Engineering, vol. 13, pp. 362-373, July 2017.

16.     C. Mishra, A. K. Samantaray and G. Chakraborty, "Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate," Mechanical Systems and Signal Processing, vol. 72-73, pp. 206-222, May 2016.

17.     Z. K. Peng, P. W. Tse and F. L. Chu, "A comparison study of improved Hilbert Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing," Mechanical Systems and Signal Processing, vol. 19, pp. 974-988, September 2005.

18.     S. Prabhakar, A. R. Mohanty and A. S. Sekhar, "Application of discrete wavelet transform for detection of ball bearing race faults," Tribology International, vol. 35, pp. 793-800, December 2002.

19.     S. Radhika, G. R. Sabareesh, G. Jagadanand and V. Sugumaran, "Precise wavelet for current signature in 3φ IM," Expert Systems with Applications, vol. 37, pp. 450-455, January 2010.

20.     J. Rafiee, P. W. Tse, A. Harifi and M. H. Sadeghi, "A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system," Expert Systems with Applications, vol. 36, pp. 4862-4875, April 2009.

21.     B. Samanta, "Gear fault detection using artificial neural networks and support vector machines with generic algorithms," Mechanical Systems and Signal Processing, vol. 18, pp. 625-644, May 2004.

22.     B. Samantha and K. R. Al-Balushi, "Artificial neural network based fault diagnostics of rolling element bearings using time-domain features," Mechanical Systems and Signal Processing, vol. 17, pp. 317-328, March 2003.

23.     V. Sugumaran, G. R. Sabareesh and K. Ramachandran, "Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support," Expert Systems with Applications, vol. 34, pp. 3090-3098, May 2008.

24.     N. Tandon and A. Choudhury, "A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings," Tribology International, vol. 32, pp. 469-480, October 1999.

25.     M. Unal, M. Onat, M. Demetgul and H. Kucuk, "Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network," Measurement, vol. 58, pp. 187-196, December 2014.

26.     R. Yan, "Base Wavelet Selection Criteria for Non-stationary Vibration Analysis in Bearing Health Diagnosis," Ph.D. dissertation, University of Massachusetts Amherst, May 2007.

27.  X. Zhang, L. Xiao and, J. Kang, "Bearing fault detection based on stochastic resonance optimized by Levenberg-Marquardt algorithm," International Journal of Performability Engineering, vol. 11, pp. 61-70, January 2015.

28.     X. Zhang and J. Zhou, "Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines," Mechanical Systems and Signal Processing, vol. 41, pp. 127-140, December 2013.

29.     X. Zhang, B. Wang and X. Chen, "Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine," Knowledge-Based Systems, vol. 89, pp. 56-85, November 2015.

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-03-01.pdf)IJPE-2018-03-01.pdf[Comparison of Conventional Method of Fault Determination with Data-Driven Approach for Ball Bearings in a Wind Turbine Gearbox]1522 Kb
 

CURRENT ISSUE

Prev Next

Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

Read more

Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

Read more

Spark-based Ensemble Learning for Imbalanced Data Classification

Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

Read more

Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

Read more

An Improved Algorithm based on Time Domain Network Evolution

Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

Read more

Auto-Tuning for Solving Multi-Conditional MAD Model

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

Read more

Smart Mine Construction based on Knowledge Engineering and Internet of Things

Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

Read more

A Mining Model of Network Log Data based on Hadoop

Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com