中国机械工程2016，Vol.27Issue(19)：2607-2613,2614,8.DOI:10.3969/j.issn.1004-132X.2016.19.009
基于自适应神经模糊推理系统模糊信息融合的采煤机截齿磨损在线监测
Online Monitoring of Shearer’s Pick Wear Based on ANFIS Fuzzy Information Fusion
摘要
Abstract
In order to realize the realtime and accurate online monitoring of the wear degree in the cutting processes,the vibration signals,acoustic emission signals and temperature signals of different wear degrees were tested and extracted,and the multi feature sample databases of different wear de-grees to the cutting signals were established.The optimal fuzzy membership function for each charac-teristic signal was calculated by the minimum ambiguity optimization model,and the method of the ANFIS multidimensional fuzzy neural network was adopted to realize the fusion of multi sensor feature informations,then the fusion results of the output confidence and weight were higher.Accord-ing to the results of the random experiments of the fusion system ,the identification degree of the cut-ting wear monitoring system based on ANFIS fuzzy information fusion is high,and the maximum er-ror of the test results is less than 6 .5%,and the results show that the system has good fusion effect and higher test accuracy.关键词
截齿/最小模糊度/自适应神经模糊推理系统/信息融合/磨损量Key words
pick/minimum ambiguity/adaptive neuro-fuzzy inference system(ANFIS)/informa-tion fusion/wear extent分类
信息技术与安全科学引用本文复制引用
张强,王海舰,李立莹,刘志恒..基于自适应神经模糊推理系统模糊信息融合的采煤机截齿磨损在线监测[J].中国机械工程,2016,27(19):2607-2613,2614,8.基金项目
国家自然科学基金资助项目(51504121) （51504121）
高等学校博士学科点专项科研基金资助项目(20132121120011) （20132121120011）
工业装备结构分析重点实验室开放基金资助项目(GZ1402) （GZ1402）
辽宁省高等学校杰出青年学者成长计划资助项目(LJQ2014036) （LJQ2014036）
辽宁省“百千万人才工程”资助项目(2014921070) （2014921070）
