NDLI: Multi-Auto Associative Neural Network based sensor validation and estimation for aero-engine

Content Provider	IEEE Xplore Digital Library
Author	Shah, B. Sarvajith, M. Sankar, B. Thennavarajan, S.
Copyright Year	2013
Description	Author affiliation: Nat. Aerosp. Labs., Bangalore, India (Shah, B.; Sarvajith, M.; Sankar, B.; Thennavarajan, S.)
Abstract	Aircraft gas turbine engine, being a complex system, uses a wide sensor network to monitor its performance for control and Engine Health Management (EHM) purposes. Both applications necessitate accurate functioning of all sensors, however due to harsh operating conditions, life and accuracy of sensors is affected. Early detection of drift in measurement or fault in sensors is important as it can help in avoiding false alarms in the EHM system. It is equally important to predict the measurement, that the sensor failed to measure, till the time sensor is replaced. An Auto Associative Neural network (AANN) based sensor validation module is an analytically-redundant sensor network, which provides continuous sensor status information and estimates the measurement value in place of faulty measurements during both online and offline data validation. The number of sensors used to monitor engine are large and it is not viable to monitor all the sensors using a single AANN. Hence in this work a novel approach is adopted for sensor validation and Estimation (SVE) where sensors are grouped into smaller sets based on their location and physical relationships between them. By breaking network into smaller groups dual benefit is achieved; first it reduces complexity arising from higher dimensionality, secondly it ensures multiple-validation of each sensor through various networks. The network is trained using data generated from a validated twin spool turbojet engine simulation model. Presented approach is validated through a simplified experiment and results show prompt fault identification and prediction of sensor value with satisfactory accuracy.
Sponsorship	Aerosp. Electron. Syst. Soc.
Starting Page	1
Ending Page	7
File Size	2821624
Page Count	7
File Format	PDF
ISSN	10887725
e-ISBN	9781467356831
DOI	10.1109/AUTEST.2013.6645076
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Engines Artificial neural networks Temperature measurement Training Data models Temperature sensors Vectors Measurement Prediction Sensor Validation Engine Health Management AANN AIC Fault Detection and Isolation
Content Type	Text
Resource Type	Article

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