Failure prediction is considered one of the most critical methods for prognostic health management and maintenance planning and scheduling of manufacturing systems. This article presents a comparative analysis and shows the main characteristics of different artificial neural network approaches used to predict failures in mechanical components. This comparative analysis is based on the use of different network architectures, training algorithms, and prediction results. Also, a brief comparison is conducted between a newly developed artificial neural network-based failure prediction algorithm and the previously developed models confirming that the new algorithm outperforms the others in terms of its ability to deal with complex manufacturing systems, the prediction accuracy, and the early predictions required to improve the maintenance plans and schedules.

Title

IEEE Xplore

Publisher

IEEE

Publisher Country

United States of America

Indexing

Scopus

Impact Factor

None

Publication Type

Online only

Volume

6

Year

2022

Pages

94-98