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Validation of FCOM profiles for aircraft engine flight data using neural networks

IR@NAL: CSIR-National Aerospace Laboratories, Bangalore


Field	Value

Title	Validation of FCOM profiles for aircraft engine flight data using neural networks

Creator	Krishnan , Anjana Ananda, CM

Subject	Aerodynamics Electronics and Electrical Engineering

Description	This paper explores the application of artificial neural network approach for aircraft engine health monitoring. The Digital Flight Data Recorder (DFDR) has volumes of data which if mined appropriately can provide valuable information about the aircraft health. The Flight Crew Operating Manual (FCOM) lays down operational profiles, which are recommended to be followed for efficient fuel usage and for minimizing maintenance effort. In the proposed system, the information from FCOM profiles and ‘known’ flight data has been fused to train a back propagation feed-forward neural network. The predictions made by the neural network regarding the expected data of required engine parameters have been used to monitor the flight data and diagnose the health of the aircraft engine in relevance to the FCOM profiles. A Matlab GUI has been developed to simulate the ‘unknown’ flight data through a Simulink model for the neural network. Data from the A320 family of aircrafts has been used for training and simulating the model and preliminary results are detailed in the paper. The simulation results exhibit that the data used is fairly healthy and show a very low level of severity of degradation with respect to the profiles studied.

Date	2011-11

Type	Conference or Workshop Item PeerReviewed

Format	application/pdf

Identifier	http://nal-ir.nal.res.in/10130/1/021_G09.pdf Krishnan , Anjana and Ananda, CM (2011) Validation of FCOM profiles for aircraft engine flight data using neural networks. In: Symposium on Applied Aerodynamics and Design of Aerospace Vehicle (SAROD 2011), November 16-18, 2011, Bangalore, India.

Relation	http://nal-ir.nal.res.in/10130/