Research on Compressive and Coding Algorithm of Bearing Vibration Signal in Wireless Transmission

Abstract:

The resources of node including the storage capacity, processing capacity, the energy supply in wireless sensor networks (WSNs) are limited, and the energy of node is mainly consumed in process of data transmission. The vibration signal which changes quickly, and has large amount of data is usually used in bearing condition monitoring. So in the process of bearing condition monitoring using WSNs, if the bearing vibration signal is acquired and transmitted, then the node will be fault for running out of node resources earlier. In order to solve the problems above, and prolong the node life, a compressive and coding algorithm of bearing vibration signal is proposed. The algorithm is based on 5/3 integer lifting wavelet, and fuses the embedded zerotree wavelet and Huffman coding algorithm. The bearing vibration data is firstly processed by 5/3 wavelet, then the wavelet coefficients obtained is compressed and coded by zerotree wavelet, and finally the results above are further compressed and coded by Huffman algorithm to improve the compressing ratio. The algorithm is programmed and transplanted in DSP of node, the evaluative criteria of compressive ratio (CR) and root mean square error (RMSE) are adopted to verify the performance of algorithm. The experiment results show that the vibration signal is compressed and coded efficiently, the main frequency feature of vibration signal is retained although the compressing ratio is up to 9.5. The amount of vibration data in wireless transmission decreases greatly, and at the same time, the memory space of host computer is saved.

• Periodical: Applied Mechanics and Materials (Volume 757)
• Pages: 195-199
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.757.195
• Online since: April 2015
• Authors: Nan Wang*, Yuan Zhang, Wei Ning
• Keywords: 5/3 Lifting Wavelet, Bearing Vibration Signal, Embedded Zerotree Wavelet, Huffman Coding Algorithm, Wireless Transmission

References

[1] H. Dong, J.G. Lu and Y.X. Sun, Distributed wavelet compression in wireless sensor networks, Chinese Journal of Sensor and Actuators. Vol. 20 (2007), pp.2481-2486.

[2] C.Y. Yan, Q.X. Yang and W.S. Liu, A real-time data compression & reconstruction method based on lifting scheme, Proceedings of the CSEE, Vol. 25 (2005), pp.6-10.

[3] C.D. Duan, Z.J. He, Feature extraction approach for rotor rub-impact using second generation wavelet transform, Turbine Technology, Vol. 48 (2006), pp.34-39.

[4] J. M. Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE Transactions on Signal Processing, Vol. 41 (1993), pp.3445-3462.

DOI: 10.1109/78.258085

[5] D. A. Huffman, I. Associate, A method for the construction of minimum-tedundancy codes, Proceedings of the Institute of Radio Engineers, Vol. 40 (1952), pp.1908-1101.

[6] G.P. Shi, The compression effect of Huffman coding data algorithm, Journal of Chizhou College, Vol. 22 (2008), pp.46-48.