工程设计学报2018，Vol.25Issue(3)：278-287,10.DOI:10.3785/j.issn.1006-754X.2018.03.005
截齿磨损程度的多特征信号融合识别研究
Research on wear degree recognition of picks based on multi feature signal fusion
摘要
Abstract
In order to solve the engineering problems of on-line monitoring and state recognition for the wear degree of picks during mining process,a method to recognize the wear degree of picks based on multi feature signal fusion was proposed .An experimental platform for recognizing the wear degree of picks was set up,and the vibration acceleration signal,acoustic e-mission signal,infrared thermal signal and motor current signal in cutting process were extracted and tested respectively .A sample library of multi-sensor data for picks cutting was established . Aiming at the problems of existing data intersection between two adjacent samples of wear states,which reduced the system recognition accuracy, the minimum fuzzyness optimization model was established to calculate the optimal fuzzy membership function of each characteristic signal and the maximum membership degree of feature samples were obtained .A neural network identification model for different wear degree of picks was constructed .The Back-Propagation (BP) neural network was studied and trained by using multi feature data samples .The experi-mental results showed that the BP network discriminant results of the recognition model were consistent with the actual type,and this recognition model could accurately monitor and recognize the type of wear degree of picks .The research results provide a solution for monitoring and repla-cing picks in practical engineering .关键词
截齿/磨损程度/振动信号/声发射信号/红外热像信号/电机电流信号Key words
pick/wear degree/vibration signal/acoustic emission signal/infrared thermal image signal/motor current signal分类
信息技术与安全科学引用本文复制引用
张强,刘志恒,王海舰,张赫哲..截齿磨损程度的多特征信号融合识别研究[J].工程设计学报,2018,25(3):278-287,10.基金项目
国家自然科学基金资助项目(51504121) （51504121）
辽宁省自然科学基金资助项目(201601324 ) （201601324 ）
煤炭资源安全开采与洁净利用工程研究中心开放课题(LNTU16KF02) （LNTU16KF02）
