Archives of Acoustics, 43, 2, pp. 163–175, 2018
10.24425/122364

Application of EMD, ANN and DNN for Self-Aligning Bearing Fault Diagnosis

Narendiranath Babu THAMBA
VIT University
India

Arun ARAVIND
VIT University
India

Abhishek RAKESH
VIT University

Mohamed JAHZAN
VIT University

Rama Prabha D.
VIT University
India

Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different
faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain and time-frequency domain features are then extracted which are used to implement the neural networks through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found to be 95.7% and using DNN has been found to be 100%.
Keywords: self-aligning bearing; fault classification; artificial neural networks; deep neural networks
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Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).

References

Ali J.B., Fnaiech N., Saidi L., Chebel-Morello B., Fnaiech, F. (2015), Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals, Applied Acoustics, 89, 16–27, https://doi.org/10.1016/j.apacoust.2014.08.016.

Boashash B. (1992a), Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals, Proceedings of the IEEE, 80, 4, 520–538, doi: 10.1109/5.135376.

Boashash B. (1992b), Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications, 80, 4, 540–568, doi: 10.1109/5.135378.

Chen Z., Deng S., Chen X., Li C., Sanchez R.-V., Qin H. (2017), Deep neural networks based rolling bearing fault diagnosis, Microelectronics Reliability, 75, 327–333; doi: 10.1016/j.microrel.2017.03.006.

Feng Z., Ma H., Zuo M. J. (2016), Amplitude and frequency demodulation analysis for fault diagnosis of planet bearings, Journal of Sound and Vibration, 382, 395–412.

Gan M., Wang C. (2016), Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings, Mechanical Systems and Signal Processing, 72–73, 92–104.

Huang N.E., Shen Z., Long S.R. (1999), A new view of nonlinear water waves: The Hilbert Spectrum, Annual Reviews of Fluid Mechanics, 31, 1, 417–457.

Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen N.-C., Tung C.C., Liu H.H. (1998), The empirical mode decomposition and the Hilbert Spectrum for non-linear and non-stationary time series analysis, Proceedings of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences, 454, 903–995, doi: 10.1098/rspa.1998.0193.

Ibrahim A., Guillet F., El Badaoui M., Bonnardot F. (2008), Techniques to estimate the instantaneous frequency with an aim of diagnosis of induction machines faults, [in:] Proceedings of 34th Annual Conference of IEEE Industrial Electronics, IECON 2008, Orlando, FL, pp. 391–396.

Jia F., Lei YL., Lin J., Zhou X., Lu N. (2016), Deep neural networks: a promising tool for fault characteristic mining intelligent diagnosis of rotating data with massive data, Mechanical Systems and Signal Processing, 72–73, 303–315, doi: 10.1016/j.ymssp.2015.10.025.

Kim T.-W., Valdés J.B. (2003), Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks, Journal of Hydrologic Engineering, 8, 6, 319–328, doi: 10.1061/(ASCE)1084-0699(2003)8:6(319).

Rato R.T., Ortigueira M.D., Batista A.G. (2008), On the HHT, its problems and some solutions, Mechanical Systems and Signal Processing, 22, 6, 1374–1394.

Samanta B., Al-Balushi K. R., Al-Araimi S.A. (2003), Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Engineering Applications of Artificial Intelligence, 16, 7–8, 657-665.

Saridakis K.M., Nikolakopoulos P.G., Papadopoulos C.A., Dentsoras A.J. (2008), Fault diagnosis of journal bearings based on artificial neural networks and measurements of bearing performance, [in:] Proceedings of the Ninth International Conference on Computational Structures Technology, B.H.V. Topping, M. Papadrakakis (Eds), Civil-Comp Press, Stirlingshire, UK, Paper 118, 2008, doi:10.4203/ccp.88.118.

Vyas N.S., Satishkumar D. (2001), Artificial Neural Network Design for fault identification in a rotor bearing system, Mechanism and Machine Theory, 36, 2, 157–175.

Wu T.Y., Lai C.H., Liu D.C. (2016), Defect diagnosis of roller bearings using instantaneous frequency normalisation under fluctuant rotating speed, Journal of Mechanical Science and Technology, 30, 3, 1037–1048.

Yang W.-X. (2008), Interpretation of mechanical signals using an improved Hilbert–Huang transform, Mechanical Systems and Signal Processing, 22, 5, 1061–1071.




DOI: 10.24425/122364