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

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.