Xinglin Li

TL;DR: In this paper, a new scheme for the prediction of a ball bearing's remaining useful life based on self-organizing map (SOM) and back propagation neural network methods is presented.

TL;DR: A comprehensive review of the PHM field is provided, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information, to enable rapid customization and integration of PHM systems for diverse applications.

TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

TL;DR: In this article, the authors synthesize and place these individual pieces of information in context, while identifying their merits and weaknesses, and discuss the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field.

TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.

TL;DR: A recurrent neural network based health indicator for RUL prediction of bearings with fairly high monotonicity and correlation values is proposed and it is experimentally demonstrated that the proposed RNN-HI is able to achieve better performance than a self organization map based method.