Research on sub-health monitoring of equipment based on industrial big data technology

Abstract

Aiming at the problems of traditional equipment fault diagnosis, such as poor real-time performance, low efficiency and strong subjectivity, a method of industrial equipment sub-health status monitoring based on big data technology was proposed. Firstly, the characteristic engineering of vibration signals is formed by preprocessing vibration signals collected by sensors located near the bearing shaft. Then, the deep residual shrinkage network DRSN was used to build the classification model. By tuning the model parameters, the optimal sub-health status identification model of industrial equipment was obtained. Finally, the accuracy, robustness and generalization of the model were verified. Experimental results show that this method has better generalization performance and can significantly improve the accuracy of industrial equipment fault diagnosis, which has important theoretical value and practical significance to solve the sub-health problems of industrial equipment.