Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review
importance 7/10
partially solved
Structural Health Monitoring Techniques aerospace stage: research
Problem
Detecting, localizing, and predicting damage in aerospace structures from complex sensor data is critical for safety.
Why it matters
Undetected structural damage in aircraft and spacecraft endangers lives and costly hardware.
What the AI did
Reviews supervised, unsupervised, deep, and hybrid machine learning methods for damage detection, localization, and prediction in aerospace SHM.
Results
not stated in source
Impact
Maps strengths and limitations of ML-based SHM and provides a research roadmap.
Record
| Venue | Sensors |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Gennaro Scarselli, Francesco Nicassio |
| Citations | 44 (OpenAlex, at import time) |
| References | 188 |
| Model or method | various ML techniques (supervised, unsupervised, deep, hybrid learning) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s25196136 |
| Open access | gold |
| Keywords | structural health monitoring, machine learning, aerospace, damage detection, review |
Importance rating
7
significant — thousands to millions affected
SHM is critical to safety of aircraft and spacecraft throughout their lifecycle; undetected damage endangers lives and costly hardware across the aerospace industry, though no scale is quantified.