Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization
Ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) requires effective fault detection and identification (FDI). Traditional multi-stage FDI methods, particularly those using residual detection layers, increase complexity and computational cost, limiting real-time applications. Thi...
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IAES
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/45310/1/Anomaly%20detection%20in%20quadcopter%20flight-Harnessing%20frequency%20domain%20analysis.pdf http://umpir.ump.edu.my/id/eprint/45310/ https://doi.org/10.11591/eei.v14i4.9224 |
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| author | Mohd Sharif, Zakaria Mohammad Fadhil, Abas Norhafidzah, Mohd Saad Mohd Herwan, Sulaiman Dwi, Pebrianti |
| author_facet | Mohd Sharif, Zakaria Mohammad Fadhil, Abas Norhafidzah, Mohd Saad Mohd Herwan, Sulaiman Dwi, Pebrianti |
| author_sort | Mohd Sharif, Zakaria |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) requires effective fault detection and identification (FDI). Traditional multi-stage FDI methods, particularly those using residual detection layers, increase complexity and computational cost, limiting real-time applications. This study proposes a single-stage anomaly detection framework integrating barnacle mating optimization (BMO) with discrete cosine transform (DCT) for UAV fault detection. While prior research explored model-based and data-driven FDI, bio-inspired optimization techniques remain underexplored in frequency-domain analysis. This study develops a BMO-based fitness function analyzing 3rd, 5th, and 7th harmonic peaks to detect UAV anomalies. Software-in-the-Loop (SITL) simulations validate the method, achieving a 5-second optimal frame size, mean absolute percentage error (MAPE) of 0.05, and root mean square error (RMSE) of 195.52. The findings confirm that a single-stage detection framework via optimization method and frequency domain analysis is possible, making it viable for real-time UAV applications. This study bridges the gap in bio-inspired UAV fault detection, paving the way for safer and more efficient UAV operations. |
| format | Article |
| id | my.ump.umpir-45310 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2025 |
| publisher | IAES |
| record_format | eprints |
| spelling | my.ump.umpir-453102025-08-08T02:04:16Z http://umpir.ump.edu.my/id/eprint/45310/ Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization Mohd Sharif, Zakaria Mohammad Fadhil, Abas Norhafidzah, Mohd Saad Mohd Herwan, Sulaiman Dwi, Pebrianti TK Electrical engineering. Electronics Nuclear engineering Ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) requires effective fault detection and identification (FDI). Traditional multi-stage FDI methods, particularly those using residual detection layers, increase complexity and computational cost, limiting real-time applications. This study proposes a single-stage anomaly detection framework integrating barnacle mating optimization (BMO) with discrete cosine transform (DCT) for UAV fault detection. While prior research explored model-based and data-driven FDI, bio-inspired optimization techniques remain underexplored in frequency-domain analysis. This study develops a BMO-based fitness function analyzing 3rd, 5th, and 7th harmonic peaks to detect UAV anomalies. Software-in-the-Loop (SITL) simulations validate the method, achieving a 5-second optimal frame size, mean absolute percentage error (MAPE) of 0.05, and root mean square error (RMSE) of 195.52. The findings confirm that a single-stage detection framework via optimization method and frequency domain analysis is possible, making it viable for real-time UAV applications. This study bridges the gap in bio-inspired UAV fault detection, paving the way for safer and more efficient UAV operations. IAES 2025-08 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/45310/1/Anomaly%20detection%20in%20quadcopter%20flight-Harnessing%20frequency%20domain%20analysis.pdf Mohd Sharif, Zakaria and Mohammad Fadhil, Abas and Norhafidzah, Mohd Saad and Mohd Herwan, Sulaiman and Dwi, Pebrianti (2025) Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization. Bulletin of Electrical Engineering and Informatics, 14 (4). pp. 3146-3160. ISSN 2302-9285. (Published) https://doi.org/10.11591/eei.v14i4.9224 https://doi.org/10.11591/eei.v14i4.9224 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Sharif, Zakaria Mohammad Fadhil, Abas Norhafidzah, Mohd Saad Mohd Herwan, Sulaiman Dwi, Pebrianti Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization |
| title | Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization |
| title_full | Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization |
| title_fullStr | Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization |
| title_full_unstemmed | Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization |
| title_short | Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization |
| title_sort | anomaly detection in quadcopter flight: harnessing frequency domain analysis and barnacle mating optimization |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/45310/1/Anomaly%20detection%20in%20quadcopter%20flight-Harnessing%20frequency%20domain%20analysis.pdf http://umpir.ump.edu.my/id/eprint/45310/ https://doi.org/10.11591/eei.v14i4.9224 https://doi.org/10.11591/eei.v14i4.9224 |
| url_provider | http://umpir.ump.edu.my/ |