Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis. - Yangde Gao; Francesco Villecco; Ming Li 002; Wanqing Song 001 (Entropy, 2017)

Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery. - Bo Wu; Yangde Gao; Songlin Feng; Theerasak Chanwimalueang (Entropy, 2018)

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory. - Yangde Gao; Cheol Hong Kim; Jong-Myon Kim (Sensors, 2021)

A Novel Fault Diagnosis Method Based on MADCNN for Rolling Bearings. - Yangde Gao; Farzin Piltan; Zahoor Ahmad; Rafia Nishat Toma; Jong-Myon Kim (MLIS, 2022)

Data-Driven Fault Classification of Induction Motor Based on Recurrence Plot and Deep Convolution Neural Network. - Rafia Nishat Toma; Yangde Gao; Jong-Myon Kim (MLIS, 2022)

A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines. - Yangde Gao; Farzin Piltan; Jong-Myon Kim (Sensors, 2022)

A Novel Image-Based Diagnosis Method Using Improved DCGAN for Rotating Machinery. - Yangde Gao; Farzin Piltan; Jong-Myon Kim (Sensors, 2022)

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features. - Rafia Nishat Toma; Yangde Gao; Farzin Piltan; Kichang Im; Dongkoo Shon; Tae Hyun Yoon; Dae-Seung Yoo; Jong-Myon Kim (Sensors, 2022)

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network. - Yangde Gao; Zahoor Ahmad; Jong-Myon Kim (Sensors, 2024)

The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder. - Yangde Gao; Zahoor Ahmad; Jong-Myon Kim (Sensors, 2024)