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

Линиите свързват координатора с партньорите.

Накратко на български

Цел на проекта

CNC machines have become an essential part of manufacturing industries. Unfortunately, unplanned downtime due to equipmentfailure causes significant losses and disrupts production. Predictive maintenance using Artificial Intelligence (AI), particularly DeepLearning (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 thedynamic nature of machine operations, data variability, and multiple sensors pose significant challenges to implementing thisapproach in real-time. Thus, I propose PreAdapt-CNC, a novel, robust, and adaptive AI framework incorporating adaptive domaindeep transfer learning, capable of accurately predicting component failures and remaining useful life for CNC machines underindustrial challenges. In this project, I will develop an IoT framework, a fault dataset for components, a fast signal and featureextraction algorithm, novel DL models, and perform real-time testing and validation of the designed framework. My project will havea significant economic impact by reducing unplanned downtime and increasing equipment lifespan. Furthermore, it aligns with theEU strategy for the sustainable development goal of “Industry, Innovation, and Infrastructure,” boosting European industrialcompetitiveness. For the project, Prof. Dimitrios Chronopoulos, a leading expert in vibration measurement, and failure prognosis atKU Leuven, is the ideal supervisor. KU Leuven's proven track record in hosting Marie Curie fellows and managing research projects willprovide me with a cooperative environment. PreAdapt-CNC will advance my career through multidisciplinary skills, industrialexposure, and specialized training. Moreover, I will also build a long-term collaboration network with European institutes, promotingknowledge exchange, innovation, and future research.

Оригинален текст от CORDIS (на английски).

Участници

• KATHOLIEKE UNIVERSITEIT LEUVEN · LeuvenКоординаторБелгия
• UNIVERSIDAD DE GRANADA · GranadaИспания

Връзки

• Виж в CORDIS
• DOI: 10.3030/101268532