Research and application of K-means clustering and DCT compression algorithm in vibration sensor

Abstract

In order to prolong the service life of wireless vibration sensors when collecting a large number of high-frequency vibration data, this paper studied the existing vibration data compression algorithms, put forward and analyzed the existing problems, and on this basis, proposed an effective mechanism of K-means clustering-discrete cosine transform (DCT) dual data compression. According to the characteristics of predictive maintenance data, K-means clustering-DCT dual compression algorithm firstly used K-means algorithm to aggregate and classify vibration data, and then carried out DCT compression according to the frequency domain characteristics of vibration signals. The verification results showed that the algorithm significantly improved the data compression efficiency and reduced the transmission of redundant data by aggregating vibration data. In addition, under the condition of the same amount of data, the algorithm had better application performance after improving the peak signal-to-noise ratio compared with other algorithms.

Keywords

• sensor
• low frequency vibration
• intermediate frequency vibration
• high frequency vibration
• k-means clustering and dct compression algorithm