Mechanical equipment safety assessment and fault diagnosis system under big data mining technology

Abstract

To solve the problem that mechanical equipment fault monitoring is difficult because of the vibration of signals and improve the accuracy of fault location during the operation of mechanical equipment, taking big data mining technology as the core, a set of mechanical equipment safety assessment and fault diagnosis system is designed. Firstly, the design scheme of the system and relevant algorithm improvement of big data mining technology are introduced. Then, on the basis of big data technology, the hardware and software of mechanical equipment fault diagnosis system are designed. Finally, according to the simulation experiment, the performance evaluation of the data optimization algorithm and the analysis of the system simulation obstacle experiment results are carried out. The results show that the Correlation Matching Extension (CME) method and the adaptive set empirical mode decomposition algorithm proposed in this research can reduce the decomposition error of signals, improve the analysis effect of rotating mechanical equipment, and locate the fault characteristics of mechanical equipment quickly. This research can promote the application of big data mining technology in mechanical field.