Diagnosis intellectualization of complex technical systems

Abstract

The article presents the results of developing a model for diagnosing a ship complex technical system with incomplete data and its implementation in an intelligent system for assessing the risk of failures of subsystems, components, intercomponent links, which allows obtaining a priori information about the technical condition of a complex system. Types of technical condition of subsystems, components, intercomponent connections are determined on the basis of diagnostic features of a complex system using the example of a ship power plant to assess the risk of their failures. Predicting the type of technical state of a complex technical system was carried out using a posteriori inference in Bayesian belief networks. The studies presented in the article assessed the risk of failures as a result of the use of an intelligent system for diagnosing and predicting the risk of failures of a ship complex technical system. The model for diagnosing and predicting the risk of failures of subsystems, components, interconnections can be considered as a conceptual model of an intelligent system for diagnosing and predicting the risk of failures of complex technical systems on network infrastructures, which has a relative insensitivity to incomplete technological data.