Xiao-Hua Li

TL;DR: The proposed algorithm is applied to parameter estimation for a PMSM drive system and results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously.

TL;DR: A deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.

TL;DR: A coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed, which can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously.

TL;DR: A hierarchical fast parallel coevolutionary immune particle swarm optimization (PSO) algorithm, accelerated by graphics processing unit (GPU) technique (G-PCIPSO), is proposed for multiparameter identification and temperature monitoring of permanent magnet synchronous machines (PMSM) as discussed by the authors.

TL;DR: Detailed comparisons and extensive experimental results show that the diagnosis performance of SPADA outperforms the existing deep learning and domain adaptation methods in dealing with the PDA problem.