Name
Title | ||
|---|---|---|
ARFA: Automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation |
Abstract | ||
|---|---|---|
In this paper a novel approach to autonomous real time flight data analysis (FDA) is proposed and investigated. The anomaly detection is based on recursive density estimation (RDE) and the fault identification is based on the evolving self-learning classifiers introduced recently. The paper starts with a brief critical analysis of the currently used FDA methods and tools. Then the problems of fault detection (FD) and identification are described formally. The importance of the ability to process the data in real time and on-line (in flight) is directly related to the efficiency and safety. Therefore, in this paper the focus is on the recursive approaches which are computationally lean and suitable for on-line mode of operation. The novel concept of ARFA (Automated Real-time FDA) is then applied to real flight data from Russian and USA made aircrafts. The results are compared and analyzed. Both, advantages that this novel methodology and algorithms offer as well as the current limitations and future directions of research are pointed out and future work outlined. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EAIS.2013.6604110 | Evolving and Adaptive Intelligent Systems |
Keywords | Field | DocType |
aerospace computing,aerospace simulation,pattern classification,pattern clustering,recursive estimation,ARFA,automated real time flight data analysis,autonomous real time flight data analysis,critical analysis,evolving clustering,fault detection,fault identification,recursive density estimation,self learning classifiers,Evolving Classifier,Fault Detection and Identification,Flight Data Analysis,Recursive Density Estimation (RDE),eClass | Density estimation,Anomaly detection,Data mining,Fault detection and isolation,Block cipher mode of operation,Flight data,Artificial intelligence,Engineering,Cluster analysis,Aerospace simulation,Recursion,Machine learning | Conference |
ISSN | Citations | PageRank |
2330-4863 | 7 | 0.79 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kolev, D. | 1 | 7 | 0.79 |
| Plamen Angelov | 2 | 954 | 67.44 |
| Markarian, G. | 3 | 7 | 0.79 |
| Suvorov, M. | 4 | 7 | 0.79 |