FAULT CLASSIFICATION BASED UPON SELF ORGANIZING FEATURE MAPS AND DYNAMIC PRINCIPAL COMPONENT ANALYSIS FOR INERTIAL SENSOR DRIFT

Fault detection and identification is an active research field in several ap- plication areas. There are still many challenges in on-line detection and identification. Over The years several approaches have been pursued based on model-based or knowledge- based techniques, however, these present several practical drawbacks with regards to time consumption or lack of adaptability. Here a mechanism to classify both previously encoun- tered faults and also new novel faults is presented. This is based upon a combination of a statistical approach, Principal Component Analysis (PCA), and non-supervised neural networks, Self Organizing Maps (SOM). Simulation results are presented through inser- tion of incipient faults into the inertial sensors of an aircraft flight control system and an evaluation of the proposed approach is made. Keywords: Fault diagnosis, Neural network, Principal component analysis, Aircraft systems

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