Multi-channel monitoring data compression method for industrial robot based on compressed sensing

To address the problems of considerable redundancy and weak transmission security of multi-channel real-time vibration monitoring data of industrial robots, in this research a compressed sensing (CS)-based method to compress multi-channel industrial robot monitoring data has been proposed. Firstly, the multi-sensor data of the robot is fused into a comprehensive signal by the cross-correlation function fusion algorithm, which can significantly reduce the signal redundancy among sensors and improve the signal quality. Then, the discrete cosine transform matrix is used for sparse decomposition analysis of the synthetic signal. Finally, CS technology and chaotic matrix are used to encrypt and compress the integrated signal to achieve efficient encryption transmission. The experimental results show that the method can significantly reduce the amount of data transmitted and enhance the encryption performance during transmission without sacrificing helpful information and ensuring signal transmission efficiency and security.