Title:
Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations
Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations
| dc.contributor.author | Puranik, Tejas G. | |
| dc.contributor.author | Jimenez, Hernando | |
| dc.contributor.author | Mavris, Dimitri N. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Aerospace Systems Design Laboratory | en_US |
| dc.date.accessioned | 2019-10-22T13:54:53Z | |
| dc.date.available | 2019-10-22T13:54:53Z | |
| dc.date.issued | 2017-01 | |
| dc.description.abstract | Among operations in the General Aviation community, one of the most important objectives is to improve safety across all flight regimes. Flight data monitoring or Flight Operations Quality Assurance programs have percolated in the General Aviation sector with the aim of improving safety by analyzing and evaluating flight data. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions. The use of data mining techniques for safety analysis, incident examination, and fault detection is gaining traction in the aviation community. In this paper, we have presented a generic methodology for identifying anomalous flight data records from General Aviation operations using energy based metrics and clustering techniques. The sensitivity of this methodology to various key parameters is quantified using different experiments. A demonstration of this methodology on a set of actual flight data records as well as simulated flight data is presented highlighting its future potential. | en_US |
| dc.description.sponsorship | Federal Aviation Administration | en_US |
| dc.identifier.citation | Puranik, T. G., Jimenez, H., & Mavris, D. N. (2017). Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations. In AIAA SciTech Forum. AIAA Information Systems-AIAA Infotech @ Aerospace . https://doi.org/10.2514/6.2017-0789 | en_US |
| dc.identifier.doi | 10.2514/6.2017-0789 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/61959 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | ASDL; | en_US |
| dc.subject | Safety | en_US |
| dc.subject | General Aviation | en_US |
| dc.subject | Energy metrics | en_US |
| dc.subject | Clustering | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations | en_US |
| dc.type | Text | |
| dc.type.genre | Paper | |
| dspace.entity.type | Publication | |
| local.contributor.author | Mavris, Dimitri N. | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.contributor.corporatename | Aerospace Systems Design Laboratory (ASDL) | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAuthorOfPublication | d355c865-c3df-4bfe-8328-24541ea04f62 | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 | |
| relation.isOrgUnitOfPublication | a8736075-ffb0-4c28-aa40-2160181ead8c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
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