Compressed sensing algorithm for machinery vibration signals based on optimal classification

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (14) : 86-93.

WANG Qiang ZHANG Pei-lin WANG Huai-guang WU Ding-hai ZHANG Yun-qiang

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Abstract

Aiming at the difficulty of dictionary construction for machinery vibration signals in the process of compressed sensing, an adaptive dictionary construction algorithm based on signal classification was put forward. Machinery vibration signals were divided into blocks according to their cutting size, and an energy sequence was produced in accordance with the energy of each signal block. Using the QPSO algorithm, the energy sequence was classified where the variance of the sequence between different classes was ensured to be the biggest. Then the classification of the signal blocks was realized by virtue of the energy sequence. Finally, the dictionary of different class of signal blocks was constructed by using the mcthod of KSVD, and the reconstruction of machinery vibration signals was achieved. The experiments on rolling bearing in different status show that the method put forward can increase the peak signal to noise ratio of reconstructed signals and improve the effects of reconstruction for machinery vibration signals.

Key words

machinery vibration signals / dictionary construction / adaptive blocked compressed sensing / optimal classification

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WANG Qiang ZHANG Pei-lin WANG Huai-guang WU Ding-hai ZHANG Yun-qiang. Compressed sensing algorithm for machinery vibration signals based on optimal classification[J]. Journal of Vibration and Shock, 2018, 37(14): 86-93

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