Title

Journal Bearing Fault Detection Based on Daubechies Wavelet

Journal title

Archives of Acoustics

Yearbook

2017

Volume

vol. 42

Issue

No 3

Authors

Narendiranath, Babu T. ; Himamshu, H.S. ; Prabin, Kumar N. ; Rama, Prabha D. ; Nishant, C.

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics

Date

2017

Identifier

DOI: 10.1515/aoa-2017-0042

Source

Archives of Acoustics; 2017; vol. 42; No 3

References

De Azevedo (2016), ujo of wind turbine bearing condition monitoring : State of the art and challenges Renewable and Sustainable, review Energy Reviews, 368. ; Chen (2016), Wavelet transform based on inner product in fault diagnosis of rotating machinery Mechanical Systems and Signal Processing, review, 70, 1. ; Narendiranath Babu (null), on Application of Dynamic Parameters of Bearing for Vibration and Condition of, Review Journal Monitoring Journal Mechanics, 15, 2015. ; Kareem (null), Evaluation of failures in mechanical crankshafts of automobile based on expert opinion Case Studies in Engineering Failure, Analysis, 11, 2015. ; Tianrui (2013), Research on fault diagnosis for TBM based on wavelet packet transforms and BP neural rd International Advance Computing Conference pp, IEEE, 20, 677. ; Dabrowski (2016), Simultaneous analysis of noise and vibration of machines in vibroacoustic diagnostics of, Archives Acoustics, 41, 783. ; Dehm (2012), Filter design aspects in analog receiver front - ends for frequency scanning applications International Symposium on Signals Systems, Electronics. ; Percival (2000), Wavelet methods for time series analysis Cambridge Series in Statistical and Probabilistic Mathematics Cambridge University, null, 16. ; Rafiee (2010), Application of mother wavelet functions for automatic gear and bearing fault diagnosis Systems with Applications, Expert, 18, 4568. ; Feng (2011), Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet, Measurement, 44, 1582. ; Guo (2012), Wind turbine tower vibration modeling and monitoring by the nonlinear state estimation technique Energies, null, 5. ; Mehdizadeh (2014), An investigation into failure analysis of interfering part of a steam turbine journal bearing Case Studies in Engineering Failure, Analysis, 14, 61. ; Springer (2004), Walnut An introduction to wavelet analysis, null, 22. ; Srinivasan (1998), High quality audio compression using an adaptive wavelet packet decomposition and psychoacoustic modelling Transaction on Signal Processing, IEEE, 19, 1085. ; Liu (2012), offer Signal analysis using wavelets for structural damage detection applied to wind energy converters in Proceedings of the th International Conference on Computing in Civil and Building, Engineering, 13, 27. ; Piotrowski (2010), On the possibility of application of the magnetoacoustic emission intensity measurements for the diagnosis of thick - walled objects in the industrial environment Measurements Technology, Science, 17, 1. ; Tse (2004), Machine fault diagnosis through an effective exact wavelet analysis of and, Journal Sound Vibration, 21, 277. ; Hariharan (2012), New approach of classification of rolling element bearing fault using artificial neural network Elixir, Mechanical Engineering, 49, 9964. ; Lazzerini (2013), Classifier ensembles to improve the robustness to noise of bearing fault diagnosis and Applications, Pattern Analysis, 12, 235. ; Xu (2009), The Stability Analysis of Hydrodynamic Journal Bearings Allowing for Manufacturing Tolerances Part Effect Analysis of Manufacturing Tolerances by Taguchi Method Proceedings of International Conference on Measuring Technology and Mechatronics Automation, null, 24, 164. ; Wang (2011), Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network, Computers Industrial Engineering, 23, 511. ; Hakim (2014), Modal parameters based structural damage detection using artificial neural networks - a Structures and, review Smart Systems, 14, 159. ; Ali (null), Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals, Applied Acoustics, 2015.