Abstract:

In order to improve the operation efficiency of the belt conveyor and reduce the downtime

caused by the fault，the key problems of the fault prediction and maintenance strategy of the belt conveyor

are analyzed in depth. By comprehensively investigating traditional methods，machine learning techniques，

and advanced prediction methods based on deep learning，the performance of different methods in fault de⁃

tection accuracy and early warning ability is compared. The research shows that the traditional method is ef⁃

fective in dealing with simple faults，and the deep learning method shows significant advantages in dealing

with complex fault modes and early prediction. The possibility of combining predictive technology to opti⁃

mize preventive and repairable maintenance strategies is further explored to improve equipment reliability

and operational efficiency. The importance of adopting data-driven and intelligent maintenance strategies

were emphasized，and pointed out that the future development trend will focus on using artificial intelli⁃

gence technology to optimize the maintenance system and realize the efficient and reliable operation of the

belt conveyor

.

Key words:

belt conveyor fault prediction, maintenance strategy, deep learning