Based on the Digital Platform of Road Construction Machinery and Equipment Maintenance Management Methods

Abstract

The practical application of data mining technology aims to provide guarantee for better fault prediction of electromechanical equipment and safe and stable operation of expressways. In order to improve the accuracy of electromechanical equipment health status recognition, an automatic health status recognition model based on data mining was constructed. Firstly, the health status data of electromechanical equipment is collected, and the wavelet transform is used to denoise the health status data of the equipment. Then, the Elman neural network in data mining technology is introduced to design the automatic health status recognition model of electromechanical equipment, and the parameters of the Elman neural network are optimized. Finally, through the simulation test of automatic health status recognition of electromechanical equipment, the results show that, the automatic recognition effect of this model is good, and the error rate and rejection rate of the health status of mechanical and electrical equipment are lower than other models, which verifies the superiority of the automatic recognition of the health status of mechanical and electrical equipment of this model. This paper analyzed the application value of data mining technolog, and the demand for fault prediction of electromechanical equipment on expressways is explained.