天津大学教师个人主页系统刘若楠 Selected Papers Home

Liu Ruonan

Associate professor

Current position:: Home >> Academic Achievements >> Selected Papers

[1] Yang B, Liu R, Chen X. Sparse time-frequency representation for incipient fault diagnosis of wind turbine drive train[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(11): 2616-2627.
[2] Yang B, Liu R, Chen X. Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD[J]. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1321-1331.
[3] Liu R, Yang B, Ma M, et al. Sparse components separation-based operational reliability assessment approach[C]//2016 Prognostics and System Health Management Conference (PHM-Chengdu). IEEE, 2016: 1-5.
[4] Liu R, Yang B, Zhang X, et al. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 75: 345-370.
[5] Liu R, Meng G, Yang B, et al. Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine[J]. IEEE Transactions on Industrial Informatics, 2016, 13(3): 1310-1320.
[6] Liu R, Yang B, Zio E, et al. Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing, 2018, 108: 33-47.
[7] Liu R, Yang B, Hauptmann A G. Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network[J]. IEEE Transactions on Industrial Informatics, 2019, 16(1): 87-96.
[8] Yang B, Liu R*, Zio E. Remaining useful life prediction based on a double-convolutional neural network architecture[J]. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9521-9530.
[9] Liu R, Wang F, Yang B, et al. Multi-scale Kernel based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Non-stationary Conditions[J]. IEEE Transactions on Industrial Informatics, 2019.

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