In order to reduce emergencies and damages to equipment, it is relevant to devise new methods for the estimations of risk and prediction of the technical condition of ship CTS. 2. Literature review and problem statement From the point of view of technical safety, quantitative assessments of the risk of CTS failures are a necessary and complex problem, which requires development and application of special mathematical apparatus. As follows from [6], the solution of applied problems of such type is frequently based on the methods of mathematical and statistical modeling, the method of analysis "tree of failures", etc. At present, among the methods for modeling the reliability, there is an advanced technology of the automated structural-logical simulation and calculation of reliability, safety of complex systems objects and processes, effectiveness and risk in the functioning of systems [7]. Reliability of ship CTS depends on a large number of factors, which characterize their design, conditions of production and operation. This leads to the fact that the 4

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The analysis of management methods Big Data is carried out. In order to obtain timely results of analyzing the state of complex technical systems on the basis of the list of parameters established by regulatory documentation that are ofparamount (for critical components) and minor importance in diagnosing the state of components ensuring the operation of complex technical systems, it is necessary to develop a method for managing data with high speed and losslessly separate and transfer Big Data from IIS to relational and non-relational databases. A method is proposed that ensures the distribution of data coming from information-measuring systems to relational and non-relational databases in diagnosing and predicting the state of complex technical systems. The expediency of using the concept of Data Mining in SCADA systems to control Big Data is substantiated. Algorithms for transmission, distribution and analysis of data in an information-measuring system for diagnosing and predicting the state of complex technical systems have been developed. The scheme of data transmission in devices using the CAN bus. The proposed method for managing Big Data in diagnosing and predicting the state of complex technical systems is based on ensuring the dynamic distribution of data in an information-measuring system with regard to the requirements imposed on the structure of the local-computer network. The method is based on the application of the principles of the construction of software-configured networks, allowing to manage the network by using the results of the analysis of data flows passing through the node-based network devices. A software implementation of a data distribution system in a local network has been developed using the principle ofanalyzing network packets as they arrive at the switching nodes of an information-measuring system. The system of program logic of data distribution from information-measuring systems transmitted over local networks or via CAN bus has been developed. From the conducted research, it follows that the best performance of the data separation process according to the developed method is achieved with distributed execution of computational processes by the developed program in four continuous modes, and the data separation process in non-relational bases for all experiments performed is faster than for relational data. The use of the developed Big Data management method with data distribution in relational and non-relational databases provides an increase in the speed of the information-measuring system in diagnosing and predicting the state of complex technical systems. Allows you to predict the technical condition of critical components of the systems during their short-term in an emergency condition, as well as to carry out a long-term forecast of the technical condition of the entire complex technical system. The use of software distributors of transmitted information provides an increase in the speed of the information-measuring system in diagnosing and predicting the state of complex technical systems, thereby ensuring timely assessment of the state of the critical components of complex technical systems whose failure affects the operation of the systems.

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