Tensor Singular Spectrum Decomposition Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis. - Cancan Yi; Yong Lv; Mao Ge; Han Xiao 008; Xun Yu (Entropy, 2017)

A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization. - Dongjie Zhong; Cancan Yi; Han Xiao 008; Houzhuang Zhang; Anding Wu (Complex., 2018)

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy. - Zhang Dang; Yong Lv; Yourong Li; Cancan Yi (Entropy, 2018)

A Joint Fault Diagnosis Scheme Based on Tensor Nuclear Norm Canonical Polyadic Decomposition and Multi-Scale Permutation Entropy for Gears. - Mao Ge; Yong Lv; Cancan Yi; Yi Zhang 049; Xiangjun Chen (Entropy, 2018)

Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis. - Yong Lv; Yi Zhang 049; Cancan Yi (Entropy, 2018)

Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification. - Yong Lv; Houzhuang Zhang; Cancan Yi (Sensors, 2018)

An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy. - Mao Ge; Yong Lv; Yi Zhang 049; Cancan Yi; Yubo Ma (Entropy, 2019)

A Novel Demodulation Analysis Technique for Bearing Fault Diagnosis via Energy Separation and Local Low-Rank Matrix Approximation. - Yong Lv; Mao Ge; Yi Zhang 049; Cancan Yi; Yubo Ma (Sensors, 2019)

A Novel Fault Feature Recognition Method for Time-Varying Signals and Its Application to Planetary Gearbox Fault Diagnosis under Variable Speed Conditions. - Yong Lv; Bingqi Pan; Cancan Yi; Yubo Ma (Sensors, 2019)

A Novel Fault Diagnosis Scheme for Rolling Bearing Based on Convex Optimization in Synchroextracting Chirplet Transform. - Guanghui You; Yong Lv; Yefeng Jiang; Cancan Yi (Sensors, 2020)

Steel Strip Defect Sample Generation Method Based on Fusible Feature GAN Model under Few Samples. - Cancan Yi; Qirui Chen; Biao Xu; Tao Huang (Sensors, 2023)