Prediction of induction motor faults using machine learning.

ABSTRACT: Unplanned downtime in industrial sectors presents significant challenges, impacting both production efficiency and profitability. To tackle this issue, companies are actively working towards optimizing their operations and reducing disruptions that hinder their ability to meet customer demands and financial goals. Predictive maintenance, utilizing advanced technologies like data analytics, machine learning, and IoT devices, offers real-time equipment data monitoring and analysis. This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. Implementing such a model can enable proactive maintenance, ultimately leading to decreased downtime in industrial operations. The study involved the acquis

SUBMITTER: Abdulkareem A 

PROVIDER: S-EPMC11759633 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature