基于奇异值分解的掘进机振动信号特征量提取
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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.
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.
引文
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[16]赵学智,聂振国,叶邦彦,等.信号有效奇异值的数量规律及其在特征提取中的应用[J].振动工程学报,2016,29(3):532-541.ZHAO Xuezhi,NIE Zhenguo,YE Bangyan,et al.Number law of effective singular values of signal and its application to feature extraction[J].Journal of Vibration Engineering,2016,29(3):532-541.
[17]何庆飞,姚春江,陈桂明,等.基于奇异值分解和小波包分析的液压泵振动信号特征提取方法[J].数据采集与处理,2012,27(2):241-247.HE Qingfei,YAO Chunjiang,CHEN Guiming,et al.Feature extraction method of hydraulic pump vibration signal based on singular value decomposition and wavelet packets analysis[J].Journal of Data Acquisition&Processing,2012,27(2):241-247.
[18]李国宾,关德林,李廷举.基于小波包变换和奇异值分解的柴油机振动信号特征提取研究[J].振动与冲击,2011,30(8):149-152.LI Guobin,GUAN Delin,LI Tingju.Feature extraction of diesel engine vibration signal based on waveletpackettransformandsingularityvalue decomposition[J].Journal of Vibration and Shock,2011,30(8):149-152.
[2]李恩龙,温保岗.我国悬臂式掘进机的发展趋势[J].煤矿机械,2013,34(5):4-7.LI Enlong,WEN Baogang.Development trend of China roadheader[J].Coal Mine Machinery,2013,34(5):4-7.
[3]李东钰,田慕琴,宋建成,等.基于最优小波基选取的掘进机振动信号去噪方法[J].工矿自动化,2016,42(10):35-39.LI Dongyu,TIAN Muqin,SONG Jiancheng,et al.Denoising method of vibration signal of roadheader based on the optimal wavelet basis selection[J].Industry and Mine Automation,2016,42(10):35-39.
[4]王维琴,李晓明,田慕琴,等.岩巷掘进机截割机构动载荷识别装置设计[J].工矿自动化,2013,39(9):16-20.WANG Weiqin,LI Xiaoming,TIAN Muqin,et al.Design of dynamic load identification device for cutting mechanism of rock roadheader[J].Industry and Mine Automation,2013,39(9):16-20.
[5]王伟,田慕琴,宋建成,等.基于小波包神经网络的岩巷掘进机动载荷识别方法[J].煤矿机械,2015,36(3):238-241.WANG Wei,TIAN Muqin,SONG Jiancheng,et al.Dynamicloadidentificationmethodofrock roadheader based on wavelet packet and neural network[J].Coal Mine Machinery,2015,36(3):238-241.
[6]聂振国.基于奇异值分解的信号处理关键技术研究[D].广州:华南理工大学,2016.
[7]童水光,唐宁,从飞云,等.基于奇异值分解拓展应用的故障特征提取技术[J].振动.测试与诊断,2017,37(1):65-69.TONG Shuiguang,TANG Ning,CONG Feiyun,et al.Rolling bearing fault feature extraction research based on applicationdevelopmentofsingularvalue decomposition[J].Journal of Vibration,Measurement&Diagnosis,2017,37(1):65-69.
[8]阮羚,李成华,宿磊,等.基于奇异值分解的局部放电模式识别方法[J].电工技术学报,2015,30(18):223-228.RUAN Ling,LI Chenghua,SU Lei,et al.Pattern recognition for partial discharging using singular value decomposition[J].Transactions of China Electrotechnical Society,2015,30(18):223-228.
[9] XING Zhanqiang,QU Jianfeng,CHAI Yi,et al.Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine[J].Korean Society of Mechanical Engineers,2017,31(2):545-553.
[10]郭谋发,徐丽兰,缪希仁,等.采用时频矩阵奇异值分解的配电开关振动信号特征量提取方法[J].中国电机工程学报,2014,34(28):4990-4997.GUO Moufa,XU Lilan,MIAO Xiren,et al.A vibration signal feature extraction method for distribution switchesbasedon singularvalue decompositionoftime-frequencymatrix[J].Proceedings of the CSEE,2014,34(28):4990-4997.
[11] LIU Yuanhong,YU Zhiwei,ZENG Ming,et al.LLE for submersible plunger pump fault diagnosis via joint wavelet and SVD approach[J].Neurocomputing,2016,185:202-211.
[12] LOOG M,DUIN R,HAEB-UMBACH R.Multiclass linear dimension reduction by weighted pairwise fisher criteria[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2001,23(7):762-766.
[13]张德丰.MATLAB小波分析[M].2版.北京:机械工业出版社,2012.
[14]李永香,牟伟杰,董峰,等.基于小波包时频相平面图及局部非负矩阵分解的内燃机故障诊断研究[J].移动电源与车辆,2016(2):41-46.LI Yongxiang,MOU Weijie,DONG Feng,et al.IC engine fault diagnosis method based on wavelet packet time-frequency phase plane image and LNMF[J].Movable Power Station&Vehicle,2016(2):41-46.
[15]陈旭,林国余.基于小波包系数稀疏表示的WFT信号去噪[J].江苏大学学报(自然科学版),2014,35(6):699-704.CHEN Xu,LIN Guoyu.Denoising for WFT signal based on sparse representation of wavelet packet coefficient[J].Journal of Jiangsu University(Natural Science Edition),2014,35(6):699-704.
[16]赵学智,聂振国,叶邦彦,等.信号有效奇异值的数量规律及其在特征提取中的应用[J].振动工程学报,2016,29(3):532-541.ZHAO Xuezhi,NIE Zhenguo,YE Bangyan,et al.Number law of effective singular values of signal and its application to feature extraction[J].Journal of Vibration Engineering,2016,29(3):532-541.
[17]何庆飞,姚春江,陈桂明,等.基于奇异值分解和小波包分析的液压泵振动信号特征提取方法[J].数据采集与处理,2012,27(2):241-247.HE Qingfei,YAO Chunjiang,CHEN Guiming,et al.Feature extraction method of hydraulic pump vibration signal based on singular value decomposition and wavelet packets analysis[J].Journal of Data Acquisition&Processing,2012,27(2):241-247.
[18]李国宾,关德林,李廷举.基于小波包变换和奇异值分解的柴油机振动信号特征提取研究[J].振动与冲击,2011,30(8):149-152.LI Guobin,GUAN Delin,LI Tingju.Feature extraction of diesel engine vibration signal based on waveletpackettransformandsingularityvalue decomposition[J].Journal of Vibration and Shock,2011,30(8):149-152.
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