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

Objective

CNC machines 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 validation of the designed framework. My project will have a significant economic impact by reducing unplanned downtime and increasing equipment lifespan. Furthermore, it aligns with the EU strategy for the sustainable development goal of “Industry, Innovation, and Infrastructure,” boosting European industrial competitiveness. For the project, Prof. Dimitrios Chronopoulos, a leading expert in vibration measurement, and failure prognosis at KU Leuven, is the ideal supervisor. KU Leuven's proven track record in hosting Marie Curie fellows and managing research projects will provide me with a cooperative environment. PreAdapt-CNC will advance my career through multidisciplinary skills, industrial exposure, and specialized training. Moreover, I will also build a long-term collaboration network with European institutes, promoting knowledge exchange, innovation, and future research.