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Homogeneous ensemble model built from artificial neural networks for fault detection in navigation systems


[ 1 ] Instytut Automatyki i Robotyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technology

Year of publication


Published in

Journal of Computational and Applied Mathematics

Journal year: 2023 | Journal volume: vol. 432

Article type

scientific article

Publication language


  • homogeneous ensemble model
  • artificial neural networks
  • quaternions
  • fault detection

EN In this paper, authors present a modern approach to the detection of malfunctioning sensory systems. The proposed solution is based on artificial neural networks. The application example uses a navigation system based on a 9-axis IMU (Inertial Measurement Unit), the signal fusion data is converted into quaternions. The form of quaternions is then analyzed along with sensor samples by an artificial neural network. If the network detects data processing inadequate to the pattern then we obtain information about the malfunction of a specific sensing axis from the sensors. The results compare fault detection capabilities using an ensemble structure built from three types of artificial neural networks: fully-connected, recurrent and convolutional. We provide a comprehensive analysis of all models; the proposed measures include RMSE (Root-Mean-Square Error), NRMSE (Normalized RMSE), t-SNE (t-distributed stochastic neighbor embedding) visualization, ROC (Receiver Operating Characteristic) curve, precision vs. recall curve, AUC (Area Under Curve) and F-score.

Date of online publication


Pages (from - to)

115279-1 - 115279-11





Article Number: 115279

Ministry points / journal


Impact Factor

2,4 [List 2022]

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