Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network
The study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through historical flight data. In additio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/10/6/565 |
| _version_ | 1797596631219044352 |
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| author | Tzu-Ying Chiu Ying-Chih Lai |
| author_facet | Tzu-Ying Chiu Ying-Chih Lai |
| author_sort | Tzu-Ying Chiu |
| collection | DOAJ |
| description | The study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through historical flight data. In addition to weather factors such as low visibility and crosswinds, human factors also account for a large part of the risk. From the accidents studied in the stochastic report of the Flight Safety Foundation, nearly 70% of the accidents occurred during the approach and landing phases, which were caused by improper control of aircraft energy. Since the information of the flight data recorder (FDR) is regarded as the airline’s confidential information, this study calculates the aircraft’s energy-related metrics and investigates the influence of non-weather-related factors on unstable approaches through a publicly available source, automatic dependent surveillance-broadcast (ADS-B) flight data. To evaluate the influence of weather- and non-weather-related factors, the outliers of each group classified by weather labels are detected and eliminated from the analysis by applying hierarchical density-based spatial clustering of applications with noise (HDBSCAN), which is utilized for detecting abnormal flights that are spatial anomalies. The deep learning method was adopted to detect and predict unstable arrival flights landing at Taipei Songshan Airport. The accuracy of the prediction for the normalized total energy and trajectory deviation of all flights is 85.15% and 82.11%, respectively. The results show that in different kinds of weather conditions, or not considering the weather, the models have similar good performance. The input features were analyzed after the model was obtained, and the flights detected as abnormal are discussed. |
| first_indexed | 2024-03-11T02:52:54Z |
| format | Article |
| id | doaj.art-816b3ed2cb9b4d11b9ea0d0e673d9799 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T02:52:54Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-816b3ed2cb9b4d11b9ea0d0e673d97992023-11-18T08:50:22ZengMDPI AGAerospace2226-43102023-06-0110656510.3390/aerospace10060565Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural NetworkTzu-Ying Chiu0Ying-Chih Lai1Institute of Civil Aviation, National Cheng Kung University, Tainan 701, TaiwanInstitute of Civil Aviation, National Cheng Kung University, Tainan 701, TaiwanThe study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through historical flight data. In addition to weather factors such as low visibility and crosswinds, human factors also account for a large part of the risk. From the accidents studied in the stochastic report of the Flight Safety Foundation, nearly 70% of the accidents occurred during the approach and landing phases, which were caused by improper control of aircraft energy. Since the information of the flight data recorder (FDR) is regarded as the airline’s confidential information, this study calculates the aircraft’s energy-related metrics and investigates the influence of non-weather-related factors on unstable approaches through a publicly available source, automatic dependent surveillance-broadcast (ADS-B) flight data. To evaluate the influence of weather- and non-weather-related factors, the outliers of each group classified by weather labels are detected and eliminated from the analysis by applying hierarchical density-based spatial clustering of applications with noise (HDBSCAN), which is utilized for detecting abnormal flights that are spatial anomalies. The deep learning method was adopted to detect and predict unstable arrival flights landing at Taipei Songshan Airport. The accuracy of the prediction for the normalized total energy and trajectory deviation of all flights is 85.15% and 82.11%, respectively. The results show that in different kinds of weather conditions, or not considering the weather, the models have similar good performance. The input features were analyzed after the model was obtained, and the flights detected as abnormal are discussed.https://www.mdpi.com/2226-4310/10/6/565flight safetyADS-BHDBSCANdeep neural networkunstable approachenergy management |
| spellingShingle | Tzu-Ying Chiu Ying-Chih Lai Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network Aerospace flight safety ADS-B HDBSCAN deep neural network unstable approach energy management |
| title | Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network |
| title_full | Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network |
| title_fullStr | Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network |
| title_full_unstemmed | Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network |
| title_short | Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network |
| title_sort | unstable approach detection and analysis based on energy management and a deep neural network |
| topic | flight safety ADS-B HDBSCAN deep neural network unstable approach energy management |
| url | https://www.mdpi.com/2226-4310/10/6/565 |
| work_keys_str_mv | AT tzuyingchiu unstableapproachdetectionandanalysisbasedonenergymanagementandadeepneuralnetwork AT yingchihlai unstableapproachdetectionandanalysisbasedonenergymanagementandadeepneuralnetwork |