Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction
The purpose of this repository is to collect the application research of deep learning in PHM field, collect and organize the open-source algorithm resources, and provide a platform for researchers to learn and communicate.
- 2020
- Dalzochio, J., et al., Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 2020. 123: p. 103298.link
- Zhao, Z., et al., Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study. ISA Transactions, 2020.link
- Jiao, J., et al., A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 2020.link
- Singh, J., et al., A systematic review of machine learning algorithms for PHM of rolling element bearings: fundamentals, concepts, and applications. Measurement Science and Technology, 2020.link
- Liu, Z. and L. Zhang, A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement, 2020. 149: p. 107002.link
- Wang, P. and R.X. Gao, Transfer learning for enhanced machine fault diagnosis in manufacturing. CIRP Annals, 2020.link
- Li, C., et al., A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing, 2020.link
- LeCun, Y., Y. Bengio and G. Hinton, Deep learning. Nature, 2015. 521: p. 436 EP -.link
- Jiao, J., et al., A comprehensive review on convolutional neural network in machine fault diagnosis. 2020.link
- Lei, Y., et al., Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 2020. 138: p. 106587.link
- Zhao, R., et al., Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 2019. 115: p. 213-237.link
- Khan, S. and T. Yairi, A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 2018. 107: p. 241-265.link
- Hoang, D. and H. Kang, A survey on Deep Learning based bearing fault diagnosis. Neurocomputing, 2019. 335: p. 327-335.link
- Liu, R., et al., Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 2018. 108: p. 33-47.link
- Lee, J., et al., Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 2014. 42(1): p. 314-334.link
- Chen, X., et al., Basic research on machinery fault diagnostics: Past, present, and future trends. Frontiers of Mechanical Engineering, 2018. 13(2): p. 264-291.link
- El-Thalji, I. and E. Jantunen, A summary of fault modelling and predictive health monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 2015. 60-61: p. 252-272.link
- Cerrada, M., et al., A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 2018. 99: p. 169-196.link
- Zhang, S., et al., Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review. arXiv preprint arXiv:1901.08247, 2019.link
- Yan, R., et al., Knowledge Transfer for Rotary Machine Fault Diagnosis. IEEE Sensors Journal: p. 1-1.link
- Lei, Y., et al., Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 2018. 104: p. 799-834.link
- Liu, W., et al., A survey of deep neural network architectures and their applications. Neurocomputing, 2017. 234: p. 11-26.link
- DL-based-Intelligent-Diagnosis-Benchmark
- Algorithm recurrence of two highly cited papers
- to prediction the remain useful life of bearing based on 2012 PHM data
- Remaining Useful Life Prediction Using RNN/LSTM/GRU Neural Networks
- Convolutional Recurrent Neural Networks for Remaining Useful Life Prediction in Mechanical Systems
- NASA-Prognostics Algorithm Library