Publications

Selected Publications:

[1] Chen D, Liu R*, Hu Q, et al. Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021.[link]

[2] Chen H,Liu R*, Xie Z, et al. Majorities Help Minorities: Hierarchical Structure Guided Transfer Learning for Few-shot Fault Recognition[J]. Pattern Recognition, 2021: 108383[link]

[3] Hu Y, Liu R*, Li X, et al. Task-Sequencing Meta Learning for Intelligent Few-Shot Fault Diagnosis with Limited Data[J]. IEEE Transactions on Industrial Informatics, 2021.[link]

[4]Wang Y, Liu R*, Lin D, et al, Coarse-to-Fine: Progressive Knowledge Transfer Based Multi-Task Convolutional Neural Network for Intelligent Large-Scale Fault Diagnosis[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021. [link]

[5] Wang F, Liu R*, Hu Q, et al. Cascade convolutional neural network with progressive optimization for motor fault diagnosis under nonstationary conditions[J]. IEEE Transactions on Industrial Informatics, 2020, 17(4): 2511-2521[link]

[6] 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.[link]

[7] 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.[link]

[8]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. (ESI, 2021 Outstanding Paper Award in IEEE TII)[link]

[9] 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. (ESI) [link]

[10] 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. (ESI) [link]

[11] 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. (ESI)[link]

[12] 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. [link]

[13] Yang B,Liu R, Chen X. Fault Diagnosis for Wind Turbine Generator Bearing via Sparse Representation and Shift-invariant K-SVD [J]. IEEE transactions on Industrial Informatics, 2016. [link]

[14] Chen D, Liu R*, Yu W, et al. Fault Diagnosis of Industrial Control System With Graph Attention Network on Multi-view Graph[C]//2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2021: 617-623. [link]

[15] Zhang K,Liu R*, Chen D, et al. Synthesize Missing Modality Based on Latent Space Model[C]//2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2021: 557-563.[link]

[16] Pu Y, Liu R*, Chen Q, et al. POC: Periodical Orthogonal Center Loss For Open Set Classification[C]//2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2021: 433-43[link]