中圖分類(lèi)號(hào)：TP212 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.222844 中文引用格式： 王昱欽，王鑫，劉保強(qiáng)，等. K-means聚類(lèi)-DCT壓縮算法在振動(dòng)傳感器中的研究與應(yīng)用[J]. 電子技術(shù)應(yīng)用，2023，49(1)：81-85. 英文引用格式： Wang Yuqin，Wang Xin，Liu Baoqiang，et al. Research and application of K-means clustering and DCT compression algorithm in vibration sensor[J]. Application of Electronic Technique，2023，49(1)：81-85.
Research and application of K-means clustering and DCT compression algorithm in vibration sensor
Wang Yuqin1，Wang Xin2，Liu Baoqiang1，Li Yi1，Hong Sheng3
1.Jiangsu Automation Research Institute， Lianyungang 222000， China； 2.Unit 32381 of the Chinese People′s Liberation Army， Beijing 100000， China； 3.School of Cyber Science and Technology， Beihang University， Beijing 100191， China
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.
Key words : sensor；low frequency vibration；intermediate frequency vibration；high frequency vibration；K-means clustering and DCT compression algorithm
