Archives of Acoustics, 42, 3, pp. 401–414, 2017
10.1515/aoa-2017-0042

Journal Bearing Fault Detection Based on Daubechies Wavelet

Narendiranath Babu THAMBA
School of Mechanical Engineering, VIT University, vellore, India
India

Himamshu H S
School of Mechanical Engineering VIT University Vellore India
India

Prabin Kumar NAYAK
School of Mechanical Engineering VIT University Vellore India
India

Rama Prabha D
School of Electrical Engineering VIT University Vellore India
India

Nishant CHILUAR
School of Mechanical Engineering VIT University Vellore India
India

Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for utomated bearing fault detection.
Keywords: journal bearing; fault diagnosis; Debauchies wavelet; artificial neural network
Full Text: PDF

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, 2, 16–27.

Chen J., Li Z., Pan J., Chen G., Zi Y., Yuan J., Chen B., He Z. (2016), Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review, Mechanical Systems and Signal Processing, 70–71, 1, 1–35.

Dabrowski Z., Dziurdz J. (2016), Simultaneous analysis of noise and vibration of machines in vibroacoustic diagnostics, Archives of Acoustics, 41, 4, 783–789.

De Azevedo H., Ara´ujo A.M., Bouchonneau N. (2016), A review of wind turbine bearing condition

monitoring: State of the art and challenges, Renewable and Sustainable Energy Reviews, 56, 2, 368–379.

Dehm-Andone G., Mzyk R., Hausknecht F., Fisher G., Weigel R., Koelpin A. (2012), Filter design aspects in analog receiver front-ends for frequency scanning applications, International Symposium on Signals, Systems, and Electronics (ISSSE).

Feng K., Jiang Z., He W., Qin Q. (2011), Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet, Measurement, 44, 9, 1582–1591.

Guo P., Infield D. (2012), Wind turbine tower vibration modeling and monitoring by the nonlinear state estimation technique (NSET), Energies, 5, 12, 5279–5293.

Hakim S.J.S., Razak H.A. (2014), Modal parameters based structural damage detection using artificial neural networks – a review, Smart Structures and Systems, 14, 2, 159–189, doi: 10.12989/sss.2014.14.2.159.

Hariharan V., Srinivasan P.S.S. (2012), New approach of classification of rolling element bearing fault using artificial neural network, Elixir Mechanical Engineering, 49, 1, 9964–9980.

Kankar P.K., Sharma S.C., Harsha S.P. (2011), Fault diagnosis of ball bearings using continuous wavelet transform, Applied Soft Computing, 11, 2, 2300–2312.

Kareem B. (2015), Evaluation of failures in mechanical crankshafts of automobile based on expert opinion, Case Studies in Engineering Failure Analysis, 3, 25–33.

Lazzerini B., Volpi S.L. (2013), Classifier ensembles to improve the robustness to noise of bearing fault diagnosis, Pattern Analysis and Applications, 16, 2, 235–251.

Liu X., Leimbach K., Hartmann D., H¨offer R. (2012), Signal analysis using wavelets for structural damage detection applied to wind energy converters, [in:] Proceedings of the 14th International Conference on Computing in Civil and Building Engineering, Moscow, Russia, 27–29 June.

Mehdizadeh M., Khodabakhshi F. (2014), An investigation into failure analysis of interfering part of a steam turbine journal bearing, Case Studies in Engineering Failure Analysis, 2, 2, 61–68.

Narendiranath Babu T., Manvel Raj T., Lakshmanan T. (2015), A Review on Application of Dynamic Parameters of Journal Bearing for Vibration and Condition Monitoring, Journal of Mechanics, 31, 4, 391–416.

Percival D.B., Walden A.T. (2000), Wavelet methods for time series analysis (Cambridge Series in Statistical and Probabilistic Mathematics), Cambridge University Press.

Piotrowski L., Augustyniak B., Chmielewski M. (2010), On the possibility of application of the magnetoacoustic emission intensity measurements for the diagnosis of thick-walled objects in the industrial environment, Measurements Science and Technology, 21, 1, 1–8.

Rafiee J., Rafiee M.A., Tse P.W. (2010), Application of mother wavelet functions for automatic gear and bearing fault diagnosis, Expert Systems with Applications, 37, 6, 4568–4579.

Srinivasan P., Jamieson L.H. (1998), High quality audio compression using an adaptive wavelet packet decomposition and psychoacoustic modelling, IEEE Transaction on Signal Processing, 46, 4, 1085–1093, doi: 10.1109/78.668558.

Tianrui Z., Zhenyu W., Tianbiao Y., Wanshan W., Haifeng Z. (2013), Research on fault diagnosis for TBM based on wavelet packet transforms and BP neural, IEEE 3rd International Advance Computing Conference (IACC), pp. 677–681.

Tse P.W., Yang W-X., Tam H.Y. (2004), Machine fault diagnosis through an effective exact wavelet analysis, Journal of Sound and Vibration, 277, 4–5, 1005–1024.

Walnut D.F. (2004), An introduction to wavelet analysis, Springer, Boston.

Wang H., Chen P. (2011), Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network, Computers & Industrial Engineering, 60, 4, 511–518.

Xu W., Ogrodnik P.J., Goodwin M.J., Bancroft G.A. (2009), The Stability Analysis of Hydrodynamic Journal Bearings Allowing for Manufacturing Tolerances. Part I. Effect Analysis of Manufacturing Tolerances by Taguchi Method, Proceedings of International Conference on Measuring Technology and Mechatronics Automation, Hunan, Vol. 2, pp. 164–167.




DOI: 10.1515/aoa-2017-0042

Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN)