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Robot arm damage detection using vibration data and deep learning

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Abstract

During robot operation, robot components like links and joints may experience collisions or excess loads that can lead to structural damages or cracks. A crack in a structural component can degrade the overall performance of the structure. This study examines the influence of cracks on the vibration characteristics of a baseline robot link. The approach uses the finite element method to simulate the dynamics of planar robot link models with and without artificial cracks with different sizes, locations, and orientations in the ABAQUS software. The robot link models include one intact model and five defective models with cracks. A rectangular crack with a fixed length of 1 mm and a varying width from 0.001 to 0.1 mm is applied to a specific location along the robot link. Finite element analysis and machine learning are used to simulate and characterize the vibration of each robot link with one fixed end and one free end. The vibration responses are measured at the free end. The measured vibration data are then transformed into two-dimensional (2D) image data using the Gramian Angular Summation Field method. A convolutional neural network is then trained with the image data for crack detection and analysis. The results indicate that the proposed method demonstrates 98.25% accuracy on the data generated by the simulation experiments.

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Data availability

The data presented in this study are available on request from the corresponding author.

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The article received no funding from any private or government organization.

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Authors and Affiliations

  1. Department of Industrial, Systems and Manufacturing Engineering, Wichita State University, 1845 Fairmount St., Wichita, KS, 67260, USA

    Getachew Ambaye, Enkhsaikhan Boldsaikhan & Krishna Krishnan

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  1. Getachew Ambaye
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  2. Enkhsaikhan Boldsaikhan
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  3. Krishna Krishnan
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Correspondence to Getachew Ambaye.

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Ambaye, G., Boldsaikhan, E. & Krishnan, K. Robot arm damage detection using vibration data and deep learning. Neural Comput & Applic 36, 1727–1739 (2024). https://doi.org/10.1007/s00521-023-09150-3

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  • DOI: https://doi.org/10.1007/s00521-023-09150-3

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