{"title":"通用航空飞行阶段识别的机器学习方法研究","authors":"Nicoletta Fala, G. Georgalis, Nastaran Arzamani","doi":"10.2514/1.i011246","DOIUrl":null,"url":null,"abstract":"Accurate identification of phases of flight is an essential step in analyses such as airport operation counts, fuel burn estimation, and safety studies. Past research has focused primarily on using positional data with rule-based or probabilistic-based decision-making to identify the phases of flight. Many of these efforts note that the task of correctly identifying phases of flight is challenging, often requiring extreme fine-tuning of methods. In this paper, we initially study whether combinations of dimensionality reduction of flight data records from general aviation aircraft impact clustering into the correct flight phases (climb, cruise, or descent) without any preprocessing or fine-tuning. For dimensionality reduction, we considered the low variance filter, the high correlation filter, principal component analysis, and autoencoders. We found that these dimensionality reduction algorithms do not offer any benefit for the phase identification task, as compared to feature selection that simply omits engine-specific features. For the clustering task, we considered [Formula: see text]-means and Gaussian mixture models. After performing clustering on eight test flights, we conclude that both methods are adequate at identifying the phases of flight in various general aviation flights and yield similar results.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"47 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on Machine Learning Methods for General Aviation Flight Phase Identification\",\"authors\":\"Nicoletta Fala, G. Georgalis, Nastaran Arzamani\",\"doi\":\"10.2514/1.i011246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate identification of phases of flight is an essential step in analyses such as airport operation counts, fuel burn estimation, and safety studies. Past research has focused primarily on using positional data with rule-based or probabilistic-based decision-making to identify the phases of flight. Many of these efforts note that the task of correctly identifying phases of flight is challenging, often requiring extreme fine-tuning of methods. In this paper, we initially study whether combinations of dimensionality reduction of flight data records from general aviation aircraft impact clustering into the correct flight phases (climb, cruise, or descent) without any preprocessing or fine-tuning. For dimensionality reduction, we considered the low variance filter, the high correlation filter, principal component analysis, and autoencoders. We found that these dimensionality reduction algorithms do not offer any benefit for the phase identification task, as compared to feature selection that simply omits engine-specific features. For the clustering task, we considered [Formula: see text]-means and Gaussian mixture models. After performing clustering on eight test flights, we conclude that both methods are adequate at identifying the phases of flight in various general aviation flights and yield similar results.\",\"PeriodicalId\":50260,\"journal\":{\"name\":\"Journal of Aerospace Information Systems\",\"volume\":\"47 1\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Aerospace Information Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.2514/1.i011246\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Aerospace Information Systems","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2514/1.i011246","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
Study on Machine Learning Methods for General Aviation Flight Phase Identification
Accurate identification of phases of flight is an essential step in analyses such as airport operation counts, fuel burn estimation, and safety studies. Past research has focused primarily on using positional data with rule-based or probabilistic-based decision-making to identify the phases of flight. Many of these efforts note that the task of correctly identifying phases of flight is challenging, often requiring extreme fine-tuning of methods. In this paper, we initially study whether combinations of dimensionality reduction of flight data records from general aviation aircraft impact clustering into the correct flight phases (climb, cruise, or descent) without any preprocessing or fine-tuning. For dimensionality reduction, we considered the low variance filter, the high correlation filter, principal component analysis, and autoencoders. We found that these dimensionality reduction algorithms do not offer any benefit for the phase identification task, as compared to feature selection that simply omits engine-specific features. For the clustering task, we considered [Formula: see text]-means and Gaussian mixture models. After performing clustering on eight test flights, we conclude that both methods are adequate at identifying the phases of flight in various general aviation flights and yield similar results.
期刊介绍:
This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.