Systém sběru diagnostických dat z průmyslových robotů

Abstract

The aim of this master’s thesis is to analyze available methods of collecting diagnostic data from industrial robots. Firstly, the theoretical aspects of predictive diagnostics and data collection from industrial robots are examined. Subsequently, two possible methods for collecting diagnostic data from the industrial robot KUKA KR3 R540 are described. The first method utilizes the diagnostic tool Trace integrated into the WorkVisual environment, while the second method reads data from the robot’s System variables. Following this, the design and implementation of an application for collecting diagnostic data are carried out, allowing independent utilization of both described data collection methods, storing the collected data, and plotting their trends. The resulting system is then tested through experiments comparing the trends of quantities measured by the mentioned methods, initially without added load and subsequently with the addition of linear or non-linear load on the robotic arm. The outcome of the work is a system that consists of an application and a robotic program, which enables the collection, storage, and plotting of diagnostic data from the KUKA industrial robot