@article {10.3844/ajeassp.2021.1.6,
article_type = {journal},
title = {Use of Lazy Wavelet and DCT for Vibration Signal Compression},
author = {Okassa, Aimé Joseph Oyobe and Ngantcha, Jean Pierre and Ndtoungou, Auguste and ELE, Pierre},
volume = {14},
number = {1},
year = {2021},
month = {Jan},
pages = {1-6},
doi = {10.3844/ajeassp.2021.1.6},
url = {https://thescipub.com/abstract/ajeassp.2021.1.6},
abstract = {In this study, we compress and decompress the vibration signals from the functioning of a ball bearing. The methodology used to compress the vibration data in this study is to reduce the size of the data by reducing the spectral redundancy of the samples. We have used the DCT, which is recognized for its representational parsimony and bleaching power. To reduce the execution time of the algorithm, we used the Lazy wavelet. This wavelet separates the original signal into two signals half the size of the original signal. Parallel processing of two halves of the original signal reduces the computational load of the algorithm. We tested (compressed and then decompressed) these signals using three compression algorithms separately under the same quantification and coding conditions. These are the algorithms based on DCT, WHT and the Lazy Wavelet associated with DCT. The comparison made on the basis of the measurements of SNR, MFD, MSE, PRD and CR allowed to retain the algorithm based on the use of the Lazy wavelet and the discrete cosine transform. The results are considered very encouraging.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}