AI and Evolutionary Computation for Intelligent Aviation Health Monitoring
Na minha lista:
| Publicado no: | Electronics vol. 14, no. 7 (2025), p. 1369 |
|---|---|
| Autor principal: | |
| Publicado em: |
MDPI AG
|
| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3188812113 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14071369 |2 doi | |
| 035 | |a 3188812113 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Kabashkin, Igor | |
| 245 | 1 | |a AI and Evolutionary Computation for Intelligent Aviation Health Monitoring | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper presents a novel framework integrating evolutionary computation and artificial intelligence for aircraft health monitoring and management systems. The research addresses critical challenges in modern aircraft maintenance through a comprehensive approach combining real-time fault detection, predictive maintenance, and multi-objective optimization. The framework employs deep learning models for fault detection, achieving about 97% classification accuracy with an F1-score of 0.97, while remaining useful life prediction yields an R2 score of 0.89 with a mean absolute error of 9.8 h. Evolutionary algorithms optimize maintenance strategies, reducing downtime and costs by up to 22% compared to traditional methods. The methodology includes robust data processing protocols, feature engineering techniques, and a modular system architecture supporting real-time monitoring and decision-making. Simulation experiments demonstrate the framework’s effectiveness in balancing maintenance objectives while maintaining high reliability. The research provides practical implementation guidelines and addresses key challenges in computational efficiency, data quality, and system integration. The results show significant improvements in maintenance planning efficiency and system reliability compared to traditional approaches. The framework’s modular design enables scalability and adaptation to various aircraft systems, offering broader applications in complex technical system maintenance. | |
| 653 | |a Modular engineering | ||
| 653 | |a Structural health monitoring | ||
| 653 | |a Evolutionary computation | ||
| 653 | |a Data processing | ||
| 653 | |a System reliability | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Modular systems | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Aviation | ||
| 653 | |a Modular design | ||
| 653 | |a Life prediction | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Real time | ||
| 653 | |a Fault detection | ||
| 653 | |a Aircraft maintenance | ||
| 653 | |a Downtime | ||
| 653 | |a Management systems | ||
| 653 | |a Evolutionary algorithms | ||
| 653 | |a Predictive maintenance | ||
| 773 | 0 | |t Electronics |g vol. 14, no. 7 (2025), p. 1369 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3188812113/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3188812113/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3188812113/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |