基于深度自编码网络的刚性罐道故障诊断
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摘要
为解决提升机刚性罐道故障诊断中故障特征提取困难的问题,结合深度自编码网络的特征提取能力优势,提出了一种基于深度自编码网络的刚性罐道故障诊断方法。以重构误差作为深度自编码网络的评价准则,在各层自编码网络之间采用反向传播的方式,逐层对网络的权值和偏置进行优化。利用得到的最优权值和偏置组成特征提取网络模型,基于该网络模型提取刚性罐道的故障特征。以SVM作为分类器实现刚性罐道的故障分类。实验结果表明,该方法提取的故障特征可识别性较好,识别率较高。
In order to solve the problem of difficulty for fault diagnosis of rigid cage guide of the hoist in fault feature extraction.Combining the advantages of feature extraction,a new fault diagnosis method of rigid cage guide is proposed based on deep auto-encoder.The reconstruction error is used as evaluation criterion for deep auto-encoder network.The weight and offset of auto-encoder network is optimized layer by layer with back propagation.The network model for feature extraction is constructed by optimal weight and offset.Then fault features of rigid cage guide are extracted based on this network.The rigid cage guide fault classification is realized by SVM used as classifier.The experimental results show that the fault feature extracted by the method is recognizable,and has higher recognition rate.
In order to solve the problem of difficulty for fault diagnosis of rigid cage guide of the hoist in fault feature extraction.Combining the advantages of feature extraction,a new fault diagnosis method of rigid cage guide is proposed based on deep auto-encoder.The reconstruction error is used as evaluation criterion for deep auto-encoder network.The weight and offset of auto-encoder network is optimized layer by layer with back propagation.The network model for feature extraction is constructed by optimal weight and offset.Then fault features of rigid cage guide are extracted based on this network.The rigid cage guide fault classification is realized by SVM used as classifier.The experimental results show that the fault feature extracted by the method is recognizable,and has higher recognition rate.
引文
[1]马天兵,王孝东,杜菲,等.基于小波包和BP神经网络的刚性罐道故障诊断[J].工矿自动化,2018,44(8):76-80.MA Tianbing,WANG Xiaodong,DU Fei,et al.Fault diagnosis of rigid cage guide based on wavelet packet and BP neural network[J].Industry and Mine Automation,2018,44(8):76-80.
[2]李占芳,肖兴明.矿井罐道偏斜检测原理及诊断方法研究[J].煤炭工程,2011,43(4):79-81.LI Zhanfang,XIAO Xingming.Study on deflection detection principle and diagnosis method of cage guide[J].Coal Engineering,2011,43(4):79-81.
[3]蒋玉强.立井刚性罐道系统的非线性耦合特性及状态评估研究[D].徐州:中国矿业大学,2011.
[4]张丽娜,李振.基于MATLAB的提升容器建模及仿真[J].矿山机械,2012,40(8):43-47.ZHANG Lina,LI Zhen.Modeling and simulation of hoisting container based on MATLAB[J].Mining&Processing E-quipment,2012,40(8):43-47.
[5]张淼.立井刚性罐道典型故障的模式识别研究[D].徐州:中国矿业大学,2015.
[6]丁雪松.立井刚性罐道的振动特性研究[D].淮南:安徽理工大学,2017.
[7]GALLOWAY L C,TILEY P M,TILEY G L.The performance of fixed guidance systems in mineshafts[J].CIM Bulletin,1982,75:847.
[8]BOSKOSKI P,GASPERIN M,PETELIN D,et al.Bearing fault prognostics using Rényi entropy based features and Gaussian process models[J].Mechanical Systems&Signal Processing,2015,52-53:327-337.
[9]QIU H,LEE J,LIN J,et al.Robust performancedegra-dation assessment methods for enhanced rolling element bearing prognostics[J].Advanced Engineering Informatics,2003,17(3-4):127-140.
[10]WIDODO Achmad,YANG Bo-Suk.Support vector machine in machine condition monitoring and fault diagnosis[J].Mechanical Systems and Signal Processing,2007,21(6):2560-2574.
[11]YANG Yu,YU Dejie,CHENG Junsheng.A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J].Journal of Sound and Vibration,2006,294(1-2):269-277.
[12]袁文军,刘飞,王晓峰,等.基于深度自编码网络的轴承故障诊断[J].噪声与振动控制,2018,38(5):208-214.YUAN Wenjun,LIU Fei,WANG Xiaofeng,et al.Bearing diagnosis based on deep neural network of auto-encoder[J].Noise and Vibration Control,2018,38(5):208-214.
[2]李占芳,肖兴明.矿井罐道偏斜检测原理及诊断方法研究[J].煤炭工程,2011,43(4):79-81.LI Zhanfang,XIAO Xingming.Study on deflection detection principle and diagnosis method of cage guide[J].Coal Engineering,2011,43(4):79-81.
[3]蒋玉强.立井刚性罐道系统的非线性耦合特性及状态评估研究[D].徐州:中国矿业大学,2011.
[4]张丽娜,李振.基于MATLAB的提升容器建模及仿真[J].矿山机械,2012,40(8):43-47.ZHANG Lina,LI Zhen.Modeling and simulation of hoisting container based on MATLAB[J].Mining&Processing E-quipment,2012,40(8):43-47.
[5]张淼.立井刚性罐道典型故障的模式识别研究[D].徐州:中国矿业大学,2015.
[6]丁雪松.立井刚性罐道的振动特性研究[D].淮南:安徽理工大学,2017.
[7]GALLOWAY L C,TILEY P M,TILEY G L.The performance of fixed guidance systems in mineshafts[J].CIM Bulletin,1982,75:847.
[8]BOSKOSKI P,GASPERIN M,PETELIN D,et al.Bearing fault prognostics using Rényi entropy based features and Gaussian process models[J].Mechanical Systems&Signal Processing,2015,52-53:327-337.
[9]QIU H,LEE J,LIN J,et al.Robust performancedegra-dation assessment methods for enhanced rolling element bearing prognostics[J].Advanced Engineering Informatics,2003,17(3-4):127-140.
[10]WIDODO Achmad,YANG Bo-Suk.Support vector machine in machine condition monitoring and fault diagnosis[J].Mechanical Systems and Signal Processing,2007,21(6):2560-2574.
[11]YANG Yu,YU Dejie,CHENG Junsheng.A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J].Journal of Sound and Vibration,2006,294(1-2):269-277.
[12]袁文军,刘飞,王晓峰,等.基于深度自编码网络的轴承故障诊断[J].噪声与振动控制,2018,38(5):208-214.YUAN Wenjun,LIU Fei,WANG Xiaofeng,et al.Bearing diagnosis based on deep neural network of auto-encoder[J].Noise and Vibration Control,2018,38(5):208-214.
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