Hypothesis testing FDI algorithm based on neural networks

The traditional cai-square hypothesis testing processes fault detection by comparing the apriori information with the observation information. If the INS goes wrong, a fault model can't provide accurate apriori information, thereby the fault detection and isolation can't be performed effectively. A new fault detection algorithm based on neural network was proposed. A neural network was trained according to INS model, so the accurate apriori information was held by the trained NN. The difference of the output between networks and model can be the parameter of fault diagnoses. The application on INS/GPS integrated navigation system demonstrates that the algorithm exhibits excellent fault detecting and identifying ability. Therefore the effective fault isolating can be performed to realize the fault tolerance navigation.