Evaluation of data quality based on Bayesian networks in railway rolling stock monitoring systems

Abstract

The purpose of the research is to evaluate the quality of data based on the Bayesian network to justify the effectiveness of the Internet of Things technology in monitoring railway rolling stock. To achieve this, performed the following tasks: presented technological schemes for monitoring railway rolling stock; built Bayesian network; created probability tables; determined conditional probabilities of control events. The existing and proposed railway rolling stock monitoring systems in the Republic of Uzbekistan are investigated. To justify the effectiveness in data quality, made an evaluation based on the Bayesian network. Conditionally selected one section with a train that serves intermediate stations. Two systems monitor this train's railcars: the Automated operational transport management system (AOTMS) and the Automatic control system for rolling stock and containers (ACSRSC). Technological operations transformed into probabilistic relations. The model defines three control events for comparison. The results showed that the quality of data of the existing monitoring system is lower than that of the proposed one. It is due to the dependence on the transfer of information among themselves to the AOTMS. In the ACSRSC system, all information, regardless of previous information, will be received individually. In addition, the level of subjective interventions for the transfer of information is also significantly reduced. The capabilities of ASMSCS are significantly superior in terms of data quality. Subsequent applications of the Internet of Things technology will help improve the quality of management decision-making in the organization of train traffic.