Username   Password       Forgot your password?  Forgot your username? 


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

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


Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search

Volume 14, Number 3, March 2018, pp. 413-424
DOI: 10.23940/ijpe.18.03.p2.413424

Kuo Chi, Jianshe Kang, Xinghui Zhang, and Zhiyuan Yang

Mechanical Engineering College, Shijiazhuang, 050003, China

(Submitted on October 12, 2017; Revised on December 8, 2017; Accepted on December 29, 2017)


Rolling bearings are the main components of modern machinery, and harsh operating environments often make them prone to failure. Therefore, detecting the incipient fault as soon as possible is useful for bearing prognostics and health management. However, the useful feature information relevant to the bearing fault contained in the vibration signals is weak under the influence of the noise and transmission path. The useful feature information is even submerged in the noise. Thus, it becomes difficult to identify the fault symptom of rolling bearings in time from the vibration signals. Stochastic resonance (SR) is a reliable method to detect the weak signal in intense noise. However, the effect of SR depends on the adjustment of two parameters. Cuckoo Search (CS) is a heuristic novel optimization algorithm that can search the global solution quickly and efficiently. Thus, CS is utilized to optimize the two parameters in this paper. Local signal-to-noise ratio (LSNR) is used to evaluate resonance effect. Two bearing fault datasets were used to confirm the effectiveness of SR optimized by CS. SR methods optimized by particle swarm optimization (PSO), genetic algorithms (GA), firefly algorithm (FA), and ant colony optimization (ACO) are also used to detect the bearing fault signal in the two datasets. The analysis results state SR optimized by CS can find better LSNR than SR optimized by other algorithms no matter if it is in the same iterations or in the same computation time, thereby making the fault feature more obvious.


References: 39

1.     P. Barthelemy, J. Bertolotti, and D. S. Wiersma, “A Levy flight for light,” Nature, vol. 453, no. 7194, pp. 495-498, 2008

2.     R. Bates, O. Blyuss, and A. Zaikin, "Stochastic resonance in an intracellular genetic perceptron," Physical Review E, (online since March 26 2014) (DOI 10.1103/PhysRevE.89.032716)

3.     R. Benzit, A. Sutera, and A. Vulpiani, “The mechanism of stochastic resonance,” Journal of Physics A: Mathematical and General, vol. 8, no. 7, pp. 62-67, 1981

4.     K. Chi, J. Kang, K. Wu, and X. Wang, “Bayesian Parameter Estimation of Weibull Mixtures Using Cuckoo Search,” in 8-th International Conference on Intelligent Networking and Collaborative Systems, pp. 411-414, Ostrava, Czech Republic, September 2016

5.     M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006

6.     F. Duan, and B. Xu, “PARAMETER-INDUCED STOCHASTIC RESONANCE AND BASEBAND BINARY PAM SIGNALS TRANSMISSION OVER AN AWGN CHANNEL,” International Journal of Bifurcation and Chaos, vol. 13, no. 2, pp. 411-425, 2003

7.     L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Review of Modern Physics, vol. 70, no. 1, pp. 223-287, 1998

8.     D. E. Goldberg, and J. H. Holland, “Genetic Algorithms and Machine Learning,” Machine Learning, vol. 3, no. 2-3, pp. 95-99, 1988

9.     D. He, X. Wang, S. Li, J. Lin, and M. Zhao, “Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis,” Mechanical Systems and Signal Processing, vol. 81, pp. 235-249, 2016

10.   Q. He, J. Wang, Y. Liu, D. Dai, and F. Kong, “Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines,” Mechanical Systems & Signal Processing, vol. 28, no. 2, pp. 443-457, 2012

11.   N. Hu, M. Chen, G. Qin, L. Xia, Z. Pan, and Z. Feng, “Extended stochastic resonance (SR) and its applications in weak mechanical signal processing,” Frontiers of Mechanical Engineering in China, vol. 4, no. 4, pp. 450-461, 2009

12.   Y. Lei, D. Han, J. Lin, and Z. He, “Planetary gearbox fault diagnosis using an adaptive stochastic resonance method,” Mechanical Systems & Signal Processing, vol. 38, no. 1, pp. 113-124, 2013

13.   Y. Lei, Z. Qiao, X. Xu, J. Lin, and S. Niu, “An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings,” Mechanical Systems & Signal Processing, vol. 94, pp. 148-164, 2017

14.   Y. G. Leng, Y. S. Leng, T. Y. Wang, and Y. Guo, “Numerical analysis and engineering application of large parameter stochastic resonance,” Journal of Sound & Vibration, vol. 292, no. 3-5, pp. 788-801, 2006

15.   J. Li, X. Chen, and Z. He, “Adaptive stochastic resonance method for impact signal detection based on sliding window,” Mechanical Systems & Signal Processing, vol. 36, no. 2, pp. 240-255, 2013

16.   M. Lin, and Y. M. Huang, “Modulation and demodulation for detecting weak periodic signal of stochastic resonance,” Acta Physica Sinica, vol. 55, no. 7, pp. 3277-3283, 2006

17.   J. J. Liu, Y. G. Leng, Z. H. Lai, and D. Tan, "Stochastic resonance based on frequency information exchange," Acta Physica Sinica, (online since November 20 2016) (DOI 10.7498/aps.65.220501)

18.   S. Lu, Q. He, H. Zhang, and F. Kong, “Rotating machine fault diagnosis through enhanced stochastic resonance by full-wave signal construction,” Mechanical Systems & Signal Processing, no. 85, pp. 82-97, 2016

19.   S. Łukasik, and S. Żak, “Firefly Algorithm for Continuous Constrained Optimization Tasks,” in International Conference on Computational Collective Intelligence, pp. 97-106, Wroclaw, Poland, October 2009

20.   M. Malik, F. Ahsan, and S. Mohsin, “Adaptive image denoising using cuckoo algorithm,” soft computing, vol. 20, no. 3, pp. 925-938, 2016

21.   B. Mcnamara, and K. Wiesenfeld, “Theory of stochastic resonance,” Physical Review A, vol. 39, no. 9, pp. 4854-4869, 1989

22.   M. K. Naik, and R. Panda, “A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition,” Applied Soft Computing, vol. 38, pp. 661-675, 2016

23.   A. Ouaarab, B. Ahiod, and X. Yang, “Discrete cuckoo search algorithm for the travelling salesman problem,” Neural Computing and Applications, vol. 24, pp. 1659-1669, 2014

24.   R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization An overview,” Swarm Intelligence, vol. 1, no. 1, pp. 33-57, 2007

25.   A. S. Raj, and N. Murali, “Morlet Wavelet UDWT Denoising and EMD based Bearing Fault Diagnosis,” Electronics, vol. 17, no. 1, pp. 1-8, 2013

26.   R. B. Randall, and J. Antoni, “Rolling element bearing diagnostics—A tutorial,” Mechanical Systems and Signal Processing, vol. 25, no. 2, pp. 485-520, 2011

27.   N. Sawalhi, R. B. Randall, and H. Endo, “The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis,” Mechanical Systems & Signal Processing, vol. 21, no. 6, pp. 2616-2633, 2007

28.   P. Shi, X. Ding, and D. Han, “Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise,” Measurement, vol. 47, pp. 540-546, 2014

29.   J. Tan, X. Chen, J. Wang, H. Chen, H. Cao, Y. Zi, and Z. He, “Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis,” Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 811-822, 2009

30.   J. Wang, Q. He, and F. Kong, “Adaptive Multiscale Noise Tuning Stochastic Resonance for Health Diagnosis of Rolling Element Bearings,” IEEE Transactions on Instrumentation & Measurement, vol. 64, no. 2, pp. 564-577, 2015

31.   X. Yang, “Nature-Inspired Metaheuristic Algorithms Second Edition,” Luniver Press, London, 2010

32.   X. Yang, and S. Deb, “Cuckoo Search via Lévy flights,” in World Congress on Nature and Biologically Inspired Computing, pp. 210-214, Coimbatore, India, December 2009

33.   X. Yang, and S. Deb, “Multiobjective cuckoo search for design optimization,” Computers & Operations Research, vol. 40, no. 6, pp. 1616-1624, 2013

34.   Y. Yang, Z. P. Jiang, B. Xu, and D. W. Repperger, “An investigation of two-dimensional parameter-induced stochastic resonance and applications in nonlinear image processing,” Journal of Physics A, vol. 42, no. 14, pp. 145207, 2009

35.   X. Zhang, N. Q. Hu, Z. Cheng, and L. Hu, “Enhanced Detection of Rolling Element Bearing Fault Based on Stochastic Resonance,” Chinese Journal of Mechanical Engineering, vol. 25, no. 6, pp. 1287-1297, 2012

36.   X. Zhang, J. Kang, J. Zhao, and H. Teng, “Rolling element bearings fault diagnosis based on correlated kurtosis kurtogram,” Journal of Vibroengineering, vol. 17, no. 6, pp. 3023-3034, 2015

37.   X. Zhang, J. Kang, L. Xiao, J. Zhao, and H. Teng, “A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis,” Shock & Vibration, vol. 2015, pp. 1-22, 2015

38.   X. H. Zhang, J. S. Kang, L. S. Hao, L. Y. Cai, and J. M. Zhao, “Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution,” Journal of Vibroengineering, vol. 17, no. 1, pp. 243-260, 2015

39.   Z. H. Zhang, D. Wang, T. Y. Wang, J. Z. Lin, and Y. X. Jiang, “Self-adaptive step-changed stochastic resonance using particle swarm optimization,” Journal of Vibration & Shock, vol. 32, no. 19, pp. 125-130, 2013


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

Download this file (IJPE-2018-03-02.pdf)IJPE-2018-03-02.pdf[Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search]1307 Kb


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.