基于奇异值分解的掘进机振动信号特征量提取  

Feature extraction of vibration signal of roadheader based on singular value decomposition

作　　者：张林锋 田慕琴[1,2] 宋建成[1,2] 贺颖[1,2] 冯君玲[1,2] 杨祥 ZHANG Linfeng;TIAN Muqin;SONG Jiancheng;HE Ying;FENG Junling;YANG Xiang(National and Local Joint Engineering Laboratory of Mining Intelligent Electrical Apparatus Technology, Taiyuan University of Technology, Taiyuan 030024, China;Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control, Taiyuan University of Technology, Taiyuan 030024, China)

机构地区：[1]太原理工大学矿用智能电器技术国家地方联合工程实验室,山西太原030024 [2]太原理工大学煤矿电气设备与智能控制山西省重点实验室,山西太原030024

出　　处：《工矿自动化》2019年第1期28-34,共7页Journal Of Mine Automation

基　　金：国家863计划资源环境技术领域重大项目(2012AA06A405);国家自然科学基金项目(U1510112)

摘　　要：针对掘进机动载荷识别难度大的问题,提出了基于奇异值分解的掘进机振动信号特征量提取方法。对采集的振动信号进行小波包分解,重构底层各频带节点系数,进而构造时频矩阵;对该矩阵进行奇异值分解,并基于Fisher判据,利用基于散度矩阵的类可分性准则,选择对不同截割岩壁硬度较为敏感的奇异值作为振动信号的特征量,并利用散度矩阵准则值来解决无法定量衡量各阶奇异值对截割硬度敏感程度的问题。与小波包频带能量法提取的特征向量进行比较,结果表明,对于掘进机水平截割、垂直截割和纵向钻进3种工况下的振动信号,基于奇异值分解法提取的特征向量都具有更好的类可分性。In view of difficulty of dynamic load identification of roadheader,feature extraction method of vibration signal of roadheader based on singular value decomposition was proposed.Collected vibration signals is decomposed by wavelet packet,and node coefficients at different frequency bands of each bottom layer are reconstructed to construct the time-frequency matrix.Then singular value decomposition of the matrix is performed,and based on Fisher criterion,class separability criterion based on divergence matrix is used to select singular value which is sensitive to hardness of different cut rock walls,and the value serves as feature quantities of the vibration signal.The criterion value of divergence matrix is used to solve the problem that it is impossible to measure quantitatively sensitivity of singular values to cutting hardness.Analysis results show that for vibration signals of roadheader under three cutting conditions of horizontal cutting,vertical cutting and longitudinal drilling,compared with feature vectors extracted by wavelet packet frequency band energy method,the feature vectors extracted by the singular value decomposition method have better class separability.

关 键 词：掘进机 动载荷识别 振动信号 特征量提取 奇异值分解 时频矩阵 小波包分解 散度矩阵准则值 截割硬度敏感程度 

分 类 号：TD421.5[矿业工程—矿山机电]