Parity Space and Pattern Recognition Approach for Hardware Redundant System Signal Validation using Artificial Neural Networks

인공신경망을 이용하여 하드웨어 다중 센서 신호 검증을 위한 패리티 공간 및 패턴인식 방법

An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.