基于CEEMD-RSVPSO-SVM的采煤机截割部滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearing in Shearer Cutting Department Based on CEEMD-RSVPSO-SVM

作　　者：程亮[1] 张步勤[2] 张金营 CHENG Liang;ZHANG Buqin;ZHANG Jinying(Handan College,Handan 056005,China;Jizhong Energy Fengfeng Group Co.,Ltd.,Handan 056107,China;China Energy Investment Corporation Limited,Beijing 100011,China)

机构地区：[1]邯郸学院,河北邯郸056005 [2]冀中能源峰峰集团有限公司,河北邯郸056107 [3]国家能源投资集团有限责任公司,北京100011

出　　处：《煤炭技术》2022年第5期179-182,共4页Coal Technology

基　　金：国家重点研发计划资助项目(2018YFB0604204)。

摘　　要：截割部作为采煤机的关键部件,直接与采煤工作面相接触,具有截煤和落煤的作用。截割部通常采用滚动轴承,滚动轴承的故障将会导致采煤机整个流程的瘫痪,为此提出一种新型滚动轴承故障诊断方法。首先采用互补集成经验模态分解对滚动轴承振动信号消噪与分解,获得轴承振动时域信号;其次采用区域划分实时调整粒子群算法中参数,并应用自适应变异操作抑制粒子群陷入局部寻优;最后采用实验室滚动轴承模拟平台验证诊断模型的有效性。结果表明,提出的滚动轴承故障诊断模型对滚动轴承故障诊断能力强、准确率高且收敛速度快。As the key component of the shearer,the cutting department is in direct contact with the coal mining face and has the functions of cutting and falling coal.The cutting department usually uses rolling bearings.The failure of the rolling bearings will cause the paralysis of shearer process.Therefore,proposes a new type of fault diagnosis method for rolling bearings.First of all,uses complementary integrated empirical mode decomposition to denoise and decompose rolling bearing vibration signals,and obtain bearing vibration time-domain signals.Secondly,the regional-division is used to adjust factor in the particle swarm algorithm.And the adaptive mutation operation is used to prevent the particle swarm from falling into local optimization.Finally,a laboratory rolling bearing simulation platform was used to verify the validity of the diagnostic model.The results show that the model proposed has strong ability to diagnose rolling bearing faults,high accuracy and fast convergence speed.

关 键 词：采煤机截割部 故障诊断 互补集成经验模态分解 自适应变异 

分 类 号：TH133.33[机械工程—机械制造及自动化]