Аннотация:
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.
