Wei Gao

TL;DR: A novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals.

TL;DR: A rolling element bearing fault diagnosis and localization approach based on multitask convolutional neural network (CNN) with information fusion is proposed, which combines domain knowledge, operating conditions, and vibration signals into a three-dimensional input that can be processed well by CNN.

TL;DR: In this paper, an opportunistic maintenance approach is proposed for wind turbines considering an imperfect maintenance schedule that is based on reliability, and a hybrid hazard rate model is used to describe imperfect maintenance and explain the effects of corrective maintenance action.

TL;DR: An opportunistic maintenance strategy for wind turbines considering stochastic weather conditions and spare parts management and the optimal maintenance and inventory strategy is obtained with the decision variables of opportunistic Maintenance reliability threshold and reorder stock level.

TL;DR: A fault diagnosis method based on continuous wavelet transform scalogram (CWTS) and Pythagorean spatial pyramid pooling convolutional neural network (PSPP-CNN) is proposed, which has higher diagnosis accuracy for variable rotating speed bearing than other methods.