Development and experimental study of analyzer to enhance maritime safety

On the basis of empirical experimental data, relationships were identified indicating the influence of navigators' response to such vessel control indicators as maneuverability and safety. This formed a hypothesis about a non-random connection between the navigator's actions, response and parameters of maritime transport management. Within the framework of this hypothesis, logical-formal approaches were proposed that allow using server data of both maritime simulators and operating vessels in order to timely identify the occurrence of a critical situation with possible catastrophic consequences. A method for processing navigation data based on the analysis of temporal zones is proposed, which made it possible to prevent manifestations of reduced efficiency of maritime transport management by 22.5 %. Based on cluster analysis and automated neural networks, it was possible to identify temporary vessel control fragments and classify them by the level of danger. At the same time, the neural network test error was only 3.1 %, and the learning error was 3.8 %, which ensures the high quality of simulation results. The proposed approaches were tested using the Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Finland). The simulation of the system for identifying critical situations in maritime transport management made it possible to reduce the probability of catastrophic situations by 13.5 %. The use of automated artificial neural networks allowed defining critical situations in real time from the database of maritime transport management on the captain's bridge for an individual navigator.