基于DL联合信息融合技术的刮板转载机故障诊断  

Fault Diagnosis of Scraper Transfer Machine Based on DL Joint with Information Fusion Technology

作　　者：武秋俊[1] 王建军[1] 郭山国[1] Wu Qiujun;Wang Jianjun;Guo Shanguo(Hebei Institute of Mechanical and Electrical Technology,Xingtai 054000,China)

机构地区：[1]河北机电职业技术学院,河北邢台054000

出　　处：《煤矿机械》2021年第2期152-154,共3页Coal Mine Machinery

摘　　要：采用深度学习(DL)算法和信息融合技术对刮板转载机进行故障诊断。对刮板转载机故障类型进行分类,建立刮板转载机故障诊断模型。为提高信息融合技术的准确率与效率,将一种典型的DL算法——深度置信网络(DBN)应用于数据层信息融合,提出基于DL联合信息融合技术的故障诊断流程。对比MATLAB仿真结果与真实情况,基于DL联合信息融合技术故障诊断结果的准确率比信息融合技术的高。Deep learning(DL)algorithm and information fusion technology were used to diagnose the fault of the scraper transfer machine.The fault types of the scraper transfer machine were classified and the fault diagnosis model of the scraper transfer machine was established.In order to improve the accuracy and efficiency of the information fusion technology,a typical DL algorithm—deep belief network(DBN)was applied to data layer information fusion and the fault diagnosis process based on DL joint with information fusion technology was proposed.Comparing the simulation results of MATLAB with the real situation,the accuracy of fault diagnosis results based on DL joint with information fusion technology is higher than that of information fusion technology.

关 键 词：DL 信息融合技术 刮板转载机 DBN 故障诊断 

分 类 号：TD528.3[矿业工程—矿山机电]