Title:
Anomaly Detection in General Aviation Operations Using Energy Metrics and Flight Data Records
Anomaly Detection in General Aviation Operations Using Energy Metrics and Flight Data Records
| dc.contributor.author | Puranik, Tejas G. | |
| dc.contributor.author | Mavris, Dimitri N. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Aerospace Systems Design Laboratory | en_US |
| dc.date.accessioned | 2020-01-02T19:17:41Z | |
| dc.date.available | 2020-01-02T19:17:41Z | |
| dc.date.issued | 2017 | |
| 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, a generic methodology is presented for identifying anomalous flight data records from General Aviation operations in the approach and landing phase. Energy based metrics, identified in previous work, are used to generate feature vectors for each flight data record. Density-based clustering and one-class classification are then used together for anomaly detection using energy-based metrics. A demonstration of this methodology on a set of actual flight data records from routine operations as well as simulated flight data is presented highlighting its potential for retrospective safety analysis. Anomaly detection using energy metrics, specifically, is a novel application presented here. | en_US |
| dc.description.sponsorship | Federal Aviation Administration (Grant No. 12-C-GA-GIT-004) | en_US |
| dc.identifier.citation | Puranik, T. G., & Mavris, D. N. (2018). Anomaly Detection in General-Aviation Operations Using Energy Metrics and Flight-Data Records. Journal of Aerospace Information Systems, 15(1), 22–36. https://doi.org/10.2514/1.I010582 | en_US |
| dc.identifier.doi | https://doi.org/10.2514/1.I010582 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/62134 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | ASDL; | en_US |
| dc.subject | Flight safety | en_US |
| dc.subject | General aviation safety | en_US |
| dc.subject | Energy metrics | |
| dc.subject | Anomaly detection | |
| dc.subject | Machine learning | |
| dc.title | Anomaly Detection in General Aviation Operations Using Energy Metrics and Flight Data Records | 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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