Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Fault diagnosis approach of rolling bearing based on NA-MEMD and FRCMAC

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 01/10/2018, Accepted 12/10/2018, Published 19/10/2018

This paper proposed a new method of fault diagnosis based on Noise Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) and Fuzzy Recurrent Cerebellar Model Articulation Controller (FRCMAC) Neural Networks. Aiming at the problem that during the use of the NA-MEMD method, the white noise amplitude parameter needs to be selected by artificial experience, a method of using Genetic Algorithm (GA) to optimize its auxiliary white noise parameters is proposed, which facilitates the use of NA-MEMD. We proposed a novel FRCMAC structure which improved Learning efficiency and dynamic response speed than traditional CMAC structure. First, the GA-NA-MEMD method is applied to process the vibration signals of rolling bearings, and the signals are decomposed into a group of Intrinsic Mode Functions (IMFs). Then use energy moments of IMFs as fault feature vectors to train FRCMAC neural network, a neural network structure suitable for rolling bearing fault diagnosis is obtained. Finally, the data from bearing data center of Case Western Reserve University is used to prove that the fault diagnosis method proposed in this paper is superior to other methods in diagnosis time and precision, which can meet the training requirements more quickly with limited training samples and fault diagnosis results more accurate.

Inspec keywords: fault diagnosis; mechanical engineering computing; rolling bearings; neurocontrollers; vibrations; white noise; machine bearings; cerebellar model arithmetic computers; genetic algorithms; acoustic signal processing

Other keywords: FRCMAC neural network; precision; noise-assisted multivariate empirical mode decomposition; IMFs; GA-NA-MEMD method; Gaussian function; intrinsic mode functions; association unit; dynamic mapping; genetic algorithm; fuzzy processing; bearing fault diagnosis; fault feature vectors; auxiliary white noise parameters; Bearing Data Center; artificial experience; FRCMAC structure; diagnosis time; fault diagnosis method; association degree; fault diagnosis results; fault diagnosis approach; traditional CMAC structure; autoregressive unit; fuzzy recurrent cerebellar model articulation controller neural networks; rolling bearings; input space; neural network structure suitable

Subjects: Vibrations and shock waves (mechanical engineering); Knowledge engineering techniques; Mechanical components; Signal processing and detection; Neural computing techniques; Digital signal processing; Other topics in statistics

References

    1. 1)
      • 2. Kordestani, M., Samadi, M.F., Saif, M., et al: ‘A new fault diagnosis of multifunctional spoiler system using integrated artificial neural network and discrete wavelet transform methods’, IEEE Sens. J., 2018, 18, (12), pp. 4990–5001.
    2. 2)
      • 14. Ma, S., Cheng, B., Shang, Z., et al: ‘Scattering transform and LSPTSVM based fault diagnosis of rotating machinery’, Mech. Syst. Signal Process., 2018, 104, pp. 155–170.
    3. 3)
      • 1. Ur Rehman, N., Park, C., Huang, N.E., et al: ‘EMD via MEMD: multivariate noise-aided computation of standard EMD’, Adv. Adapt. Data Anal., 2013, 05, (2), p. 1350007.
    4. 4)
      • 7. Liu, J., Zio, E.: ‘A scalable fuzzy support vector machine for fault detection in transportation systems’, Expert Syst. Appl., 2018, 102, pp. 36–43.
    5. 5)
      • 16. Bin, G.F., Gao, J.J., Li, X.J., et al: ‘Early fault diagnosis of rotating machinery based on wavelet packets-empirical mode decomposition feature extraction and neural network’, Mech. Syst. Signal Process., 2012, 27, pp. 696–711.
    6. 6)
      • 11. Zhong, J.-H., Wong, P.K., Yang, Z.-X.: ‘Fault diagnosis of rotating machinery based on multiple probabilistic classifiers’, Mech. Syst. Signal Process., 2018, 108, pp. 99–114.
    7. 7)
      • 5. An, X., Yang, J.: ‘A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy’, Proc. Inst. Mech. Eng., Part E, 2016, 231, (2), pp. 317–328.
    8. 8)
      • 4. Park, C., Plank, M., Snider, J., et al: ‘EEG gamma band oscillations differentiate the planning of spatially directed movements of the arm versus eye: multivariate empirical mode decomposition analysis’, IEEE Trans. Neural Syst. Rehabil. Eng., 2014, 22, (5), pp. 1083–1096.
    9. 9)
      • 13. Fadda, M.L., Moussaoui, A.: ‘Hybrid SOM–PCA method for modeling bearing faults detection and diagnosis’, J. Braz. Soc. Mech. Sci. Eng., 2018, 40, (5), p. 268.
    10. 10)
      • 12. Zhao, M., Kang, M., Tang, B., et al: ‘Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes’, IEEE Trans. Ind. Electron., 2018, 65, (5), pp. 4290–4300.
    11. 11)
      • 17. ‘Case Western Reserve University’. Available at http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website, accessed 8 January 2018.
    12. 12)
      • 10. Ciabattoni, L., Ferracuti, F., Freddi, A., et al: ‘Statistical spectral analysis for fault diagnosis of rotating machines’, IEEE Trans. Ind. Electron., 2018, 65, (5), pp. 4301–4310.
    13. 13)
      • 8. Wen, L., Li, X., Gao, L., et al: ‘A new convolutional neural network-based data-driven fault diagnosis method’, IEEE Trans. Ind. Electron., 2018, 65, (7), pp. 5990–5998.
    14. 14)
      • 15. Kuo, Y.-C., Hung, C.-P., Wu, J.-W., et al: ‘Diagnosis system design using CMAC-based scheme for automobile automatic transmission’, Int. Symp. on Computers, Consumers and Control (IS3C), Xian, China, 2016, pp. 744–747.
    15. 15)
      • 3. Rehman, N., Mandic, D.P.: ‘Multivariate empirical mode decomposition’, Proc. R. Soc. A: Math., Phy. Eng. Sci., 2009, 466, (2117), pp. 1291–1302.
    16. 16)
      • 6. Liu, R., Yang, B., Zio, E., et al: ‘Artificial intelligence for fault diagnosis of rotating machinery: a review’, Mech. Syst. Signal Process., 2018, 108, pp. 33–47.
    17. 17)
      • 9. Karami, M., Wang, L.: ‘Fault detection and diagnosis for nonlinear systems: A new adaptive Gaussian mixture modeling approach’, Energy Build., 2018, 166, pp. 477–488.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.8991
Loading

Related content

content/journals/10.1049/joe.2018.8991
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address