Journal Bearing Fault Detection Based on Daubechies Wavelet
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.
Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN)