Decision trees-based methods identification damages is a M.Tech project topic for Aerospace Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Decision trees-based methods identification damages Project Details
| Abstract |
Effective damage identification and localization are critical in industrial engineering, enabling timely corrective actions to minimize system downtime, reduce operational costs, facilitate rapid maintenance, and prevent catastrophic failures. Structural Health Monitoring (SHM) has significantly benefited from advancements in machine learning, offering enhanced capabilities for managing complex system integrity. This research investigates damage detection in composite plates specifically designed for vibration damping in aerospace applications. Experimental testing of these strongly damped structures presents a unique challenge, as the significant damping characteristics result in highly similar vibration responses between undamaged and damaged states, obscuring distinct damage signatures. To address this challenge, machine learning methodologies are employed. The study focuses on decision tree-based
algorithms, including standalone decision trees and two boosting techniques, AdaBoost and RUSBoost, selected for their robust classification performance and inherent feature selection capabilities. These methods demonstrate considerable efficacy in accurately identifying the presence of damage, distinguishing between different damage types such as thickness damage and debonding, and further classifying specific sub-types (e.g., types A and B thickness/debonding).
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| Reference Paper |
Decision trees-based methods for the identification of damages in strongly damped plates for aerospace applications |
| Domain |
Aerospace Engineering |
| Sub-Domain |
Structures & Systems / Aerospace Structures |
| PDF Download |
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