AI-Powered Fault Diagnosis for Axial Piston Pumps Using Digital Twins and Noise Signals

This research explores a practical application of digital twins and AI for predictive maintenance in a specific industrial context. The use of fluid-borne noise signals for fault diagnosis represents a potentially valuable, non-invasive approach.

• •Applies digital twin technology for predictive maintenance.
• •Utilizes fluid-borne noise signals for fault detection.
• •Focuses on a zero-shot learning approach, potentially reducing the need for extensive labeled data.