带式输送机故障诊断方法研究
霍曼，马艺琰，马鸿
（陕西工业职业技术学院航空工程学院，陕西 咸阳 712000）
摘要： 为解决矿用带式输送机故障检测和诊断分辨率不高、精确度低的问题，通过多信息融合算法，提出BP 神经网
络与D-S 证据理论相结合的算法，建立了多级故障诊断结构，优化了D-S 证据论证信度函数权重分配，降低了故障诊断
的模糊性，同时提高了矿用带式输送机故障诊断的精确性，减少了带式输送机故障的发生。
关键词： 带式输送机；故障诊断；BP神经网络；D-S 证据理论
中图分类号： TD63⁺4.1     文献标志码： A     文章编号： 2095-0802-(2022)08-0122-03
Fault Diagnosis Methods of Belt Conveyors
HUO Man, MA Yiyan, MA Hong
(School of Aeronautical Engineering, Shaanxi Polytechnic Institute, Xianyang 712000, Shaanxi, China)
Abstract: In order to solve the problems of low resolution and low accuracy in fault detection and diagnosis of mining belt
conveyors, through multi-information fusion algorithm, this paper proposed an algorithm combining BP neural network with D-S
evidence theory, established a multi-level fault diagnosis structure, optimized the weight distribution of D-S evidence demonstration
reliability function, reduced the fuzziness of fault diagnosis, improved the accuracy of fault diagnosis of mining belt conveyors, and
reduced the occurrence of belt conveyor faults.

Key words: belt conveyor; fault diagnosis; BP neural network; D-S evidence theory