A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions

Machine learning methods are widely used for rolling bearing fault diagnosis. Most of them are based on a basic assumption that training and testing data are adequate and follow the same distributi...

[1]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[2]  Jean-Marie Flaus,et al.  A model based approach to assess the performance of production systems in degraded mode , 2017 .

[3]  Zhi-Jie Yan,et al.  A context-sensitive-chunk BPTT approach to training deep LSTM/BLSTM recurrent neural networks for offline handwriting recognition , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[4]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[5]  Patrick Siarry,et al.  A postural information based biometric authentification system employing S-transform, radial basis network and Kalman filtering , 2010 .

[6]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[7]  Vikash Gilja,et al.  Sequence Transfer Learning for Neural Decoding , 2017, bioRxiv.

[8]  Zhu Huijie,et al.  Fault diagnosis of hydraulic pump based on stacked autoencoders , 2015, 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI).

[9]  Ming Shao,et al.  Generalized Transfer Subspace Learning Through Low-Rank Constraint , 2014, International Journal of Computer Vision.

[10]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[11]  Zhiheng Li,et al.  A cross domain feature extraction method based on transfer component analysis for rolling bearing fault diagnosis , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[12]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[13]  Myeongsu Kang,et al.  Highly reliable state monitoring system for induction motors using dominant features in a two-dimension vibration signal , 2013, New Rev. Hypermedia Multim..

[14]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[15]  Lianru Gao,et al.  CNN-based Large Scale Landsat Image Classification , 2018, 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

[16]  Xuelong Li,et al.  Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance , 2014, IEEE Transactions on Cybernetics.

[17]  Robert X. Gao,et al.  Digital Twin for rotating machinery fault diagnosis in smart manufacturing , 2018, Int. J. Prod. Res..

[18]  Diego Cabrera,et al.  Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .

[19]  Zhuang Fengqing,et al.  Patients’ Responsibilities in Medical Ethics , 2016 .

[20]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[21]  Zhiqiang Que,et al.  Application of Transfer Learning in Continuous Time Series for Anomaly Detection in Commercial Aircraft Flight Data , 2018, 2018 IEEE International Conference on Smart Cloud (SmartCloud).

[22]  Guo Chen,et al.  Sharing pattern feature selection using multiple improved genetic algorithms and its application in bearing fault diagnosis , 2019 .

[23]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[24]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[25]  Diego Cabrera,et al.  Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis , 2015, Neurocomputing.

[26]  Michael J. Roan,et al.  Anomaly detection in rolling element bearings via hierarchical transition matrices , 2014 .

[27]  V. Sugumaran,et al.  Fault diagnosis of bearings through vibration signal using Bayes classifiers , 2014, Int. J. Comput. Aided Eng. Technol..

[28]  Christian Biemann,et al.  Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter , 2018, ArXiv.

[29]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[30]  Shuzhi Sam Ge,et al.  Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption , 2014, IEEE Sensors Journal.

[31]  Domingo Biel Solé,et al.  Energy-balance control of PV cascaded multilevel grid-connected inverters for phase-shifted and level-shifted pulse-width modulations , 2012 .

[32]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[33]  Kenji Suzuki,et al.  A deep CNN based transfer learning method for false positive reduction , 2018, Multimedia Tools and Applications.

[34]  Sebastian Ruder,et al.  Universal Language Model Fine-tuning for Text Classification , 2018, ACL.

[35]  Yew-Soon Ong,et al.  Deep transfer learning for classification of time-delayed Gaussian networks , 2015, Signal Process..

[36]  Iyad Rahwan,et al.  Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm , 2017, EMNLP.

[37]  Abdesselam Bouzerdoum,et al.  Efficient training algorithms for a class of shunting inhibitory convolutional neural networks , 2005, IEEE Transactions on Neural Networks.

[38]  Tao Zhang,et al.  Bearing fault diagnosis method based on stacked autoencoder and softmax regression , 2015, 2015 34th Chinese Control Conference (CCC).

[39]  Peter J. Fleming,et al.  Bayesian Hierarchical Models for aerospace gas turbine engine prognostics , 2015, Expert Syst. Appl..

[40]  Li Lin,et al.  Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).