Transfer Learning Strategies for Deep Learning-based PHM Algorithms

[1]  Chao Liu,et al.  Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application , 2018, ISA transactions.

[2]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[3]  Haidong Shao,et al.  Rolling bearing fault diagnosis using an optimization deep belief network , 2015 .

[4]  Mei Wang,et al.  Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.

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

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

[7]  Ibrahim N. Tansel,et al.  Fault diagnosis on material handling system using feature selection and data mining techniques , 2014 .

[8]  Liang Gao,et al.  A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[10]  Nan Chen,et al.  Prognostics and Health Management: A Review on Data Driven Approaches , 2015 .

[11]  Bin Yang,et al.  An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings , 2019, Mechanical Systems and Signal Processing.

[12]  Tao Zhang,et al.  Deep Model Based Domain Adaptation for Fault Diagnosis , 2017, IEEE Transactions on Industrial Electronics.