A New Approach to FPGA-Implementation of DWT Applied to Real Time Denoising of Vibration Signals Related to Bearing Defects
Wavelet based denoising excels in extracting hidden diagnostic information and enhancing non-stationary signals. Implementing this technique for denoising sensed vibrations before their transmission allows reducing the size of monitoring and diagnosis applications and increasing transmission capacity, processing speed, and results accuracy. This paper presents a new approach for hardware implementation of real time Discrete Wavelet Transform for signal denoising. The first step of this approach is the choice of the wavelet bases and coefficients shrinkage settings in order to meet the requirements of vibration signal related to bearings defects in an electrical machine. Then, hardware optimization is obtained by using only two FIR filters based on a single LUT and controlled by a finite state machine. Hence, the resulting Field Programmable Gate Arrays design complexity is greatly reduced providing a huge economy of used resources while maintaining a very satisfactory execution rate.
Copyright © 2016 Praise Worthy Prize - All rights reserved.
L. Navarro, M. Delgado, J. Urresty, J. Cusidó, and L. Romeral, "Condition monitoring system for characterization of electric motor ball bearings with distributed fault using fuzzy inference tools," in Instrumentation and Measurement Technology Conference (I2MTC), 2010 IEEE, 2010, pp. 1159-1163.
Y. K. Chaudhari, J. A. Gaikwad, J.V. Kulkarni “Vibration Analysis for Bearing Fault detection in Electrical Motors” IEEE 978-1-4799-3486-7 – 2014
R. B. Randall and J. Antoni, "Rolling element bearing diagnostics—a tutorial," Mechanical Systems and Signal Processing, vol. 25, pp. 485-520, 2011.
A. Boulenger and C. Pachaud, Aide-mémoire Surveillance des machines par analyse des vibrations: Dunod, 2009 pp. 207-228
T. Loutas and V. Kostopoulos, Utilising the wavelet transform in condition-based maintenance: A review with applications: INTECH Open Access Publisher, 2012.
S. Delvecchio, On the Use of Wavelet Transform for Practical Condition Monitoring Issues: INTECH Open Access Publisher, 2012.
I. Daubechies, S. Mallat, and A. S. Willsky, "Special issue on wavelet transforms and multiresolution signal analysis-introduction," ed: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 345 E 47TH ST, NEW YORK, NY 10017-2394, 1992.
Z. E. Gketsis, M. E. Zervakis, and G. Stavrakakis, "Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN," Electric Power Systems Research, vol. 79, pp. 1483-1494, 2009.
S. N. V. Purushotham, Suryanarayana A.N. Prasad, "Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition," Elsevier, vol. NDT&E International, pp. 654–664, 10 April 2005.
Al-Haj, A. M. (2003). Fast discrete wavelet transformation using FPGAs and distributed arithmetic. International Journal of Applied Science and Engineering, 1(2), 160-171.
Chilo, J., & Lindblad, T. (2008). Hardware implementation of 1D wavelet transform on an FPGA for infrasound signal classification. Nuclear Science, IEEE Transactions on, 55(1), 9-13.
Ordaz-Moreno, A., de Jesus Romero-Troncoso, R., Vite-Frias, J. A., Rivera-Gillen, J. R., & Garcia-Perez, A. (2008). Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation. Industrial Electronics, IEEE Transactions on, 55(5), 2193-2202.
S.L. Sabat, P. Rangababu, K.P.Karthik “ System on chip implementation of 1-D Wavelet transform based denoising of fiber Optic Gyroscope signal on FPGA” IEEE 2011
A. Saeed and H. F. Ragai, "Implementation of fast discrete wavelet transform for vibration analysis on an FPGA," in Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2012 8th International Symposium on, 2012, pp. 1-5.
Cabal-Yepez, E., Garcia-Ramirez, A. G., Romero-Troncoso, R. J., Garcia-Perez, A., & Osornio-Rios, R. A. (2013). Reconfigurable monitoring system for time-frequency analysis on industrial equipment through STFT and DWT. Industrial Informatics, IEEE Transactions on, 9(2), 760-771.
M. Bahoura and H. Ezzaidi, "FPGA-implementation of discrete wavelet transform with application to signal denoising," Circuits, Systems, and Signal Processing, vol. 31, pp. 987-1015, 2012.
C. S. Burrus, R. A. Gopinath, and H. Guo, "Introduction to wavelets and wavelet transforms: a primer," 1997.
O. Rioul and P. Duhamel, "Fast algorithms for discrete and continuous wavelet transforms," Information Theory, IEEE Transactions on, vol. 38, pp. 569-586, 1992.
Mallat, S. G. (1989). A theory of multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(7), 674–693.
A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-time signal processing: Prentice-hall New Jersey, 2011 -3th edition. pp.198-204 (parallel filters)
K. Shukla and A. K. Tiwari, Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems: Springer Science & Business Media, 2013.
P. K. Meher, "New approach to look-up-table design and memory-based realization of FIR digital filter," Circuits and Systems I: Regular Papers, IEEE Transactions on, vol. 57, pp. 592-603, 2010.
P. P. Chu, FPGA prototyping by VHDL examples: Xilinx Spartan-3 version: John Wiley & Sons, 2011. pp. 127-159
V. Pedroni “Timed (Category 2) State Machines ” » in Finite state machines in hardware theory and design, MIT press, Massachsetts institute technology , Englend 2013 chp.8, pp.143- 206.
D. L. Donoho and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," Journal of the american statistical association, vol. 90, pp. 1200-1224, 1995.
D. L. Donoho, "De-noising by soft-thresholding," Information Theory, IEEE Transactions on, vol. 41, pp. 613-627, 1995.
El Yousfi Alaoui, M., Jilbab, A., El Hani, S., FPGA Design and Implementation of an Optimized Adaptive Filter for Real Time Extraction of Vibration Signal Related to Bearing Defects, (2016) International Review on Modelling and Simulations (IREMOS), 9 (2), pp. 105-113.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2021 Praise Worthy Prize