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.

Background/Objectives: The vibratory signals delivered by rotating machines are very important in the maintenance of these machines. For maintenance purposes they are stored or transmitted. The storage and transmission of these signals pose problems of space and bandwidth. To solve this problem compression is a solution. Methods/Statistical analysis: In this work, we compress and decompress the vibration signals formed by variations of the amplitudes vibration of a ball bearing. We used an algorithm based on the Walsh-Hadamard Transform (WHT) in two passes. The coefficients obtained are coded according to Huffman's coding. An evaluation of performances of this algorithm is made on the basis of the measurements of SNR, MFD, MSE, PRD and CR. Findings: Compression ratios are high when we consider that the reconstruction is almost perfect. Usually, compression methods by transformation have a nonzero reconstructed error. However, this bleaching of vibratory signals both in the temporal and frequency domain, followed by good quantization precision, allowed to cancel this error. In view of these qualitative and quantitative evaluation parameters of the method result, it can be said that the method gives very good results. Novelty/Applications: Improved of Compression Ratio of vibratory signals for maintenance purposes.