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.

العنوان

IEEE Xplore

الناشر

IEEE

بلد الناشر

الولايات المتحدة الأمريكية

Indexing

Scopus

معامل التأثير

None

نوع المنشور

إلكتروني فقط

المجلد

6

السنة

2022

الصفحات

94-98