Monitoring the state of robotic systems based on time series analysis

Abstract

In view of the close integration of robotic systems into industrial and technological systems, critical infrastructure objects, as well as a significant number of possible entry points, the task of monitoring operational safety for robotic systems is more complex than ensuring information security in classical information systems. The paper presents a method for monitoring the state of robotic systems based on time series analysis. The developed method differs from the existing ones by the combined approach of using an ensemble of parallel classifiers and Fishburn weight coefficients in the security event management system. The time series is composed of a set of informative features, characterizing the functioning of a robotic system. Values for previous discrete time points are ranked using significance weights. The method was approved on a data set of a real industrial system. Due to parallel computing, it was possible to significantly increase the speed of determining the state of robotic systems. The identification precision due to the combined approach increased by 1.45 % compared to the best results presented in scientific papers, the recall increased by 4.45 % and amounted to 99.85 % for both indicators. The results of the study can be applied in monitoring the safety of robotic systems.