Title
Comparison of immunity-based schemes for aircraft failure detection and identification.
Abstract
In this paper, two approaches are proposed and compared for the detection and identification of aircraft subsystem failures based on the artificial immune system paradigm combined with the hierarchical multiself strategy. The first approach relies on the heuristic ranking of lower order self/non-self projections and the generation of selective immunity identifiers through structuring of the non-self. The second approach is based on an information processing algorithm inspired by the functionality of the dendritic cells. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets information from the multiple selves to produce a unique detection and identification outcome. A hierarchical multi-self strategy is used with both approaches considering 2-dimensional self/non-self projections or subselves. A mathematical formulation of the concepts and detailed implementation algorithms are presented. The proposed methodologies are demonstrated and compared using simulation data for a supersonic fighter from a motion-based flight simulator at nominal conditions, under failures of actuators, malfunction of sensors, and wing damage. In all cases considered, both detection and identification schemes achieve excellent detection and identification rates with practically no false alarms.
Year
DOI
Venue
2016
10.1016/j.engappai.2016.02.017
Eng. Appl. of AI
Keywords
Field
DocType
Bioinspired failure detection,Fault identification,Artificial intelligence,Aircraft subsystems failures,Artificial immune system
Heuristic,Artificial immune system,Information processing,Ranking,Identifier,Computer science,Flight simulator,Artificial intelligence,Fuse (electrical),Machine learning,Actuator
Journal
Volume
Issue
ISSN
52
C
0952-1976
Citations 
PageRank 
References 
1
0.35
15
Authors
5
Name
Order
Citations
PageRank
Dia Al Azzawi120.71
Hever Moncayo220.71
Mario G. Perhinschi3408.06
Andres Perez410.35
Adil Togayev510.35