Abstract
Many approaches for fault detection in industrial processes has been presented in the literature, with approaches spanning from traditional univariate statistics to complex models able to encode the multivariate nature of time-series data. Although the vast corpus of works on this topic, there is no public benchmark shared among the community that can serve as a testbench for these methods, allowing researchers to evaluate their proposed approach with other state of the art approaches. In this paper we present the Industrial Robot Anomaly Detection (IndRAD) dataset as a benchmark for evaluating fault detection algorithms on industrial robots. The dataset is composed by 13 nominal trajectories and 3 trajectories with structural anomalies. We also propose a protocol to inject sensory anomalies in clean data. The dataset, code to reproduce these experiments and a leaderboard table to be used for future research are available at https://github.com/franzsetti/IndRAD.
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Acknowledgment
This work was supported by the Italian MIUR through the project “Dipartimenti di Eccellenza 2018-2022”.
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Fiorini, E., Tonin, D., Setti, F. (2023). IndRAD: A Benchmark for Anomaly Detection on Industrial Robots. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_54
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DOI: https://doi.org/10.1007/978-3-031-37742-6_54
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