Autonomous Damage Source Monitoring in Composite Structures

EasyChair Preprint 11450

Abstract

This research focuses on the application of Acoustic Emission (AE) for condition monitoring of safety-critical engineering structures. AE is a non-destructive testing technique that detects defects and structural changes by analyzing elastic energy released during crack initiation and propagation. The proposed methodology involves strategically deploying AE sensors on structures, acquiring continuous data during operation, and using advanced signal processing and pattern recognition techniques for fault detection and severity assessment. Integration with machine learning enhances accuracy and enables real-time decision-making for proactive maintenance, ensuring safer and more reliable infrastructure and industrial operations. The study emphasizes the significance of AE in extending structural life, minimizing downtime, and reducing maintenance costs. Overall, AE-based condition monitoring offers promising potential for safeguarding critical engineering assets and promoting proactive maintenance practices.

Keyphrases: acoustic emission, condition monitoring, machine learning, safety-critical structures, signal processing

@booklet{EasyChair:11450,
  author    = {Shirsendu Sikdar and Rakesh Mishra and Karl Walton},
  title     = {Autonomous Damage Source Monitoring in Composite Structures},
  howpublished = {EasyChair Preprint 11450},
  year      = {EasyChair, 2023}}