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Detection of anomalous behavior in a robot system based on deep learning elements

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Abstract

The preprocessing procedure for anomalous behavior of robot system elements is proposed in the paper. It uses a special kind of a neural network called an autoencoder to solve two problems. The first problem is to decrease the dimensionality of the training data using the autoencoder to calculate the Mahalanobis distance, which can be viewed as one of the best metrics to detect the anomalous behavior of robots or sensors in the robot systems. The second problem is to apply the autoencoder to transfer learning. The autoencoder is trained by means of the target data which corresponds to the extreme operational conditions of the robot system. The source data containing the normal and anomalous observations derived from the normal operation conditions is reconstructed to the target data using the trained autoencoder. The reconstructed source data is used to define a optimal threshold for making decision on the anomaly of the observation based on the Mahalanobis distance.

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    L. V. Utkin, V. S. Zaborovskii & S. G. Popov

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  1. L. V. Utkin
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  2. V. S. Zaborovskii
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  3. S. G. Popov
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Correspondence to L. V. Utkin.

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Original Russian Text © L.V. Utkin, V.S. Zaborovskii, S.G. Popov, 2016, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.

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Utkin, L.V., Zaborovskii, V.S. & Popov, S.G. Detection of anomalous behavior in a robot system based on deep learning elements. Aut. Control Comp. Sci. 50, 726–733 (2016). https://doi.org/10.3103/S0146411616080319

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  • DOI: https://doi.org/10.3103/S0146411616080319

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