基于栈式稀疏自编码器的矿用变压器故障诊断
|
摘要
鉴于将深度学习应用于变压器故障诊断具有良好的故障诊断效果,提出了一种基于栈式稀疏自编码器的矿用变压器故障诊断方法。通过在自编码器隐含层引入稀疏项限制构成稀疏自编码器,再将多个稀疏自编码器进行堆叠形成栈式稀疏自编码器,并以Softmax分类器作为输出层,建立矿用变压器故障诊断模型;利用大量无标签样本对模型进行无监督预训练,并通过有监督微调优化模型参数。算例分析结果表明,与栈式自编码器相比,栈式稀疏自编码器应用于矿用变压器故障诊断具有更高的准确率。
In view of application of deep learning to transformer fault diagnosis had a good fault diagnosis effect,a fault diagnosis method of mind-used transformer based on stacked sparse auto-encoder was proposed.Sparse auto-encoder is constructed by introducing sparse item constraint in hidden layer of auto-encoder,then the multiple sparse auto-encoders are stacked to form stacked sparse auto-encoder,and Softmax classifier is used as output layer to establish mine-used transformer fault diagnosis model.A large number of unlabeled samples are used to carry out unsupervised pre-training for the model,and the model parameters are optimized through supervised fine-tuning.The example analysis results show that stacked sparse auto-encoder is more accurate than stack auto-encoder in application of fault diagnosis of mind-used transformer.
In view of application of deep learning to transformer fault diagnosis had a good fault diagnosis effect,a fault diagnosis method of mind-used transformer based on stacked sparse auto-encoder was proposed.Sparse auto-encoder is constructed by introducing sparse item constraint in hidden layer of auto-encoder,then the multiple sparse auto-encoders are stacked to form stacked sparse auto-encoder,and Softmax classifier is used as output layer to establish mine-used transformer fault diagnosis model.A large number of unlabeled samples are used to carry out unsupervised pre-training for the model,and the model parameters are optimized through supervised fine-tuning.The example analysis results show that stacked sparse auto-encoder is more accurate than stack auto-encoder in application of fault diagnosis of mind-used transformer.
引文
[1]ROGERS R R.IEEE and IEC codes to interpret incipient faults in transformers using gas in oil analysis[J].IEEE Transactions on Electrical Insulation,1978,13(5):349-354.
[2]李建坡.基于油中溶解气体分析的电力变压器故障诊断技术的研究[D].长春:吉林大学,2008.
[3]杜文霞,吕锋,句希源.基于BP神经网络的电力变压器故障诊断[J].变压器,2007,44(3):45-47.DU Wenxia,LYU Feng,JU Xiyuan.Fault diagnosis of power transformer based on BP neural network[J].Transformer,2007,44(3):45-47.
[4]MATHUR A,FOODY G M.Multiclass and binary SVM classification:implications for training and classification users[J].IEEE Geoscience and Remote Sensing Letters,2008,5(2):241-245.
[5]石鑫,朱永利.深度学习神经网络在电力变压器故障诊断中的应用[J].电力建设,2015,36(12):116-122.SHI Xin,ZHU Yongli.Application of deep learning neural network in fault diagnosis of power transformer[J].Electric Power Construction,2015,36(12):116-122.
[6]刘林凡.深度学习在故障诊断中的研究综述[J].新型工业化,2017,7(4):45-48.LIU Linfan.Review of deep learning in fault diagnosis[J].The Journal of New Industrialization,2017,7(4):45-48.
[7]余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804.YU Kai,JIA Lei,CHEN Yuqiang,et al.Deep learning:yesterday,today,and tomorrow[J].Journal of Computer Research and Development,2013,50(9):1799-1804.
[8]石鑫,朱永利,宁晓光,等.基于深度自编码网络的电力变压器故障诊断[J].电力自动化设备,2016,36(5):122-126.SHI Xin,ZHU Yongli,NING Xiaoguang,et al.Transformer fault diagnosis based on deep autoencoder network[J].Electric Power Automation Equipment,2016,36(5):122-126.
[9]SHIN H C,ORTON M R,COLLINS D J,et al.Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(8):1930-1943.
[10]孙文珺,邵思羽,严如强.基于稀疏自动编码深度神经网络的感应电动机故障诊断[J].机械工程学报,2016,52(9):65-71.SUN Wenjun,SHAO Siyu,YAN Ruqiang.Induction motor fault diagnosis based on deep neural network of sparse auto-encoder[J].Journal of Mechanical Engineering,2016,52(9):65-71.
[11]王勇,赵俭辉,章登义,等.基于稀疏自编码深度神经网络的林火图像分类[J].计算机工程与应用,2014,50(24):173-177.WANG Yong,ZHAO Jianhui,ZHANG Dengyi,et al.Forest fire image classification based on deep neural network of sparse autoencoder[J].Computer Engineering and Applications,2014,50(24):173-177.
[12]OLSHAUSEN B A,FIELD D J.Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J].Nature,1996,381(6583):607-609.
[13]仲志丹,樊浩杰,李鹏辉.基于稀疏自编码神经网络的抽油机井故障诊断[J].西安科技大学学报,2018,38(4):669-675.ZHONG Zhidan,FAN Haojie,LI Penghui.Fault diagnosis of pumping well based on sparse autoencoder neural network.[J].Journal of Xi'an University of Science and Technology,2018,38(4):669-675.
[14]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536.
[15]袁莉芬,宁暑光,何怡刚,等.基于改进SAESOFTMAX的模拟电路故障诊断方法[J].电子测量与仪器学报,2018,32(7):36-44.YUAN Lifen,NING Shuguang,HE Yigang,et al.Fault diagnosis method for analog circuit based on improved SAE-SOFTMAX[J].Journal of Electronic Measurement and Instrumentation,2018,32(7):36-44.
[2]李建坡.基于油中溶解气体分析的电力变压器故障诊断技术的研究[D].长春:吉林大学,2008.
[3]杜文霞,吕锋,句希源.基于BP神经网络的电力变压器故障诊断[J].变压器,2007,44(3):45-47.DU Wenxia,LYU Feng,JU Xiyuan.Fault diagnosis of power transformer based on BP neural network[J].Transformer,2007,44(3):45-47.
[4]MATHUR A,FOODY G M.Multiclass and binary SVM classification:implications for training and classification users[J].IEEE Geoscience and Remote Sensing Letters,2008,5(2):241-245.
[5]石鑫,朱永利.深度学习神经网络在电力变压器故障诊断中的应用[J].电力建设,2015,36(12):116-122.SHI Xin,ZHU Yongli.Application of deep learning neural network in fault diagnosis of power transformer[J].Electric Power Construction,2015,36(12):116-122.
[6]刘林凡.深度学习在故障诊断中的研究综述[J].新型工业化,2017,7(4):45-48.LIU Linfan.Review of deep learning in fault diagnosis[J].The Journal of New Industrialization,2017,7(4):45-48.
[7]余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804.YU Kai,JIA Lei,CHEN Yuqiang,et al.Deep learning:yesterday,today,and tomorrow[J].Journal of Computer Research and Development,2013,50(9):1799-1804.
[8]石鑫,朱永利,宁晓光,等.基于深度自编码网络的电力变压器故障诊断[J].电力自动化设备,2016,36(5):122-126.SHI Xin,ZHU Yongli,NING Xiaoguang,et al.Transformer fault diagnosis based on deep autoencoder network[J].Electric Power Automation Equipment,2016,36(5):122-126.
[9]SHIN H C,ORTON M R,COLLINS D J,et al.Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(8):1930-1943.
[10]孙文珺,邵思羽,严如强.基于稀疏自动编码深度神经网络的感应电动机故障诊断[J].机械工程学报,2016,52(9):65-71.SUN Wenjun,SHAO Siyu,YAN Ruqiang.Induction motor fault diagnosis based on deep neural network of sparse auto-encoder[J].Journal of Mechanical Engineering,2016,52(9):65-71.
[11]王勇,赵俭辉,章登义,等.基于稀疏自编码深度神经网络的林火图像分类[J].计算机工程与应用,2014,50(24):173-177.WANG Yong,ZHAO Jianhui,ZHANG Dengyi,et al.Forest fire image classification based on deep neural network of sparse autoencoder[J].Computer Engineering and Applications,2014,50(24):173-177.
[12]OLSHAUSEN B A,FIELD D J.Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J].Nature,1996,381(6583):607-609.
[13]仲志丹,樊浩杰,李鹏辉.基于稀疏自编码神经网络的抽油机井故障诊断[J].西安科技大学学报,2018,38(4):669-675.ZHONG Zhidan,FAN Haojie,LI Penghui.Fault diagnosis of pumping well based on sparse autoencoder neural network.[J].Journal of Xi'an University of Science and Technology,2018,38(4):669-675.
[14]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536.
[15]袁莉芬,宁暑光,何怡刚,等.基于改进SAESOFTMAX的模拟电路故障诊断方法[J].电子测量与仪器学报,2018,32(7):36-44.YUAN Lifen,NING Shuguang,HE Yigang,et al.Fault diagnosis method for analog circuit based on improved SAE-SOFTMAX[J].Journal of Electronic Measurement and Instrumentation,2018,32(7):36-44.