SOURCE-LINKED INTELLIGENCE

Predictive Maintenance Using Adaptive Domain Deep Transfer Learning: Enhancing Real-Time Fault Identification and Remaining Useful Life Prediction in CNC Machines

CORDIS · observation · Publication date unknown

s have become an essential part of manufacturing industries. Unfortunately, unplanned downtime due to equipment failure causes significant losses and disrupts production. Predictive maintenance using Artificial Intelligence (AI), particularly Deep Learning (DL), offers a solution by handling complex data, extracting hidden correlations, and predicting failures accurately. However, DL models often lack adaptability when applied to different machines or environments. Moreover, the complexities introduced by the dynamic nature of machine operations, data variability, and multiple sensors pose significant challenges to implementing this approach in real-time. Thus, I propose PreAdapt-CNC, a novel, robust, and adaptive AI framework incorporating adaptive domain deep transfer learning, capable of accurately predicting component failures and remaining useful life for CNC machines under industrial challenges. In this project, I will develop an IoT framework, a fault dataset for components, a fast signal and feature extraction algorithm, novel DL models, and perform real-time testing and vali

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recordType

award

status

SIGNED

region

EU

value

216240

unit

EUR