Autonomous Damage Source Monitoring in Composite Structures

This research is focused on exploring the utilization of Acoustic Emission (AE) in the condition monitoring of safety-critical engineering structures. AE serves as a non-destructive testing method that identifies defects and structural changes by analyzing the release of elastic energy during the initiation and progression of cracks. The proposed methodology revolves around the strategic placement of AE sensors on structures, continuous data acquisition during operational phases, and the application of advanced signal processing and pattern recognition techniques for the detection of faults and assessment of their severity. The incorporation of machine learning techniques enhances accuracy and facilitates real-time decision-making for proactive maintenance, ultimately ensuring the safety and reliability of infrastructure and industrial operations. The study underscores AE's pivotal role in extending the life of structures, minimizing downtime, and reducing maintenance expenses. In summary, AE-based condition monitoring presents significant potential for safeguarding critical engineering assets and advancing proactive maintenance practices.