Methodology for creating a dataset for predictive analysis of an industrial robot

Industrial robots are one of the ways to increase production volumes. Bundling, milling, welding, laser processing, and 3D printing are a number of processes that require maintaining high precision positioning of industrial robots throughout the entire operation cycle. This article analyzes the use of the Denavit-Harterberg (DH) method to determine the positioning and orientation errors of an industrial robot. In this study, the DH method is used to create a model of possible errors in industrial robots and to create a database of deviations of the links and the working body of the robot from a predetermined trajectory. Special attention is paid to the presentation of practical steps to create a synthetic data set for the deviation of axes of an industrial robot, starting from the kinematic model of the robot and ending with the preparation of the final data format for subsequent analysis and the construction of a predictive analytics model. The importance of careful data preparation is highlighted by examples from other research in the field of predictive analytics of industrial equipment, demonstrating the economic benefits of timely detection and prevention of possible equipment failures. The developed model is used in the future to generate a synthetic data set for the deviation of the axes of an industrial robot. The proposed data collection model and methodology for creating a data set for predictive analytics are being tested on a six-axis robot designed for this purpose.

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