This paper describes the use of artificial neural networks (ANNs) with the vibration data from real flight tests for detecting engine health condition - mass imbalance herein. Order-tracking data, calculated from time series is used as the input to the neural networks to determine the amount and location of mass imbalance on aircraft engines. Several neural network methods, including multilayer perceptron (MLP), extended Kalman filter (EKF) and support vector machines (SVMs) are used in the neural network inverse model for the performance comparison. The promising performances are presented at the end.
X. Hu et al., "Vibration Analysis Via Neural Network Inverse Models to Determine Aircraft Engine Unbalance Condition," Proceedings of the International Joint Conference on Neural Networks, 2003, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at https://doi.org/10.1109/IJCNN.2003.1224049
International Joint Conference on Neural Networks, 2003
Electrical and Computer Engineering
Keywords and Phrases
Aerospace Computing; Aerospace Engines; Aircraft Engine Unbalance Condition; Artificial Neural Networks; Condition Monitoring; Engine Health Condition; Extended Kalman Filter; Fault Diagnosis; Mass Imbalance; Mechanical Engineering Computing; Multilayer Perceptron; Neural Nets; Neural Network Inverse Model; Neural Network Inverse Models; Order Tracking Data; Real Flight Tests; Support Vector Machines; Time Series; Vibration Analysis; Vibration Data; Vibration Measurement
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.