Analysis and structuring diagnostic large volume data of technical condition of complex equipment in transport

Abstract

The paper presents the results of the classification analysis model for structuring of processed large volumes of heterogeneous diagnostic data about the technical state of complex equipment in transport development and research. Concept for the description and structuring of big data is proposed based on the formation of a metadata scheme using logical breakdown of all technical diagnostic data on the output variable - the technical condition of complex technical equipment in transport. A functional assessment of the technical condition complex technical system’s elements in transport is developed based on the application of methods for assessing structural and functional risks of failures. The article presents the results of assessing the accuracy of the input data sets classification using created decision trees models to effectively structuring and presenting the data in order to ensure that the procedures for their further analysis are performed. As a result of using the developed simulation model of structuring and presenting large heterogeneous diagnostic data volumes about the state of complex technical equipment in transport the time costs were reduced and the efficiency of analytical operations to study data for solving diagnostic problems and predicting complex system’s technical condition was improved.

Data Mining Information System for Complex Technical Systems Failure Risk Evaluation

“…The use of information, analytical and intellectual capabilities in the systems of diagnostics and forecasting with the determination of reliability level allows for high-quality design and maintenance CTS equipment [2,9,10]. The use of a data mining information system (DMIS) of the risk of CTS failures, based on knowledge, fast processing and analysis of large volumes of heterogeneous information provides an adequate approach to design, reliable operation, and high-quality diagnostics of systems [11].…”

“…Such problems lead to an increase in the labor intensity and financial costs of diagnostics, forecasting at the stages of CTS operation and design. The solution of problems within the framework of the DMIS is possible by using methods and models that use the results of simulation and fuzzy modeling [2,9,10]. The successful implementation of diagnostics and forecasting CTS risk of failures is achieved by the use of achievements in the ISDF, in particular, the technologies of expert systems (ES) using knowledge bases (KB) [2].…”

“…The analysis of publications [27,28] showed also that the development of information technologies allows solving the problem of assessing the failure risk of CTS with a large elements number, with the display of systems in the directed graphs form with structural and functional relationships and interactions of elements [2,13,14]. To study the CTS model, among the many existing methods of reliability modeling, it is recommended to use a relatively developed technology of cognitive simulation (CS) of the CTS failure risk [15,16].…”

“…Examples of CTS are telecommunications, energy, transport systems, oil and gas production, mining and processing industries. Ensuring reliability is possible by using methods, diagnostic tools and predicting the technical state of systems at the stages of CTS design and operation [1,2]. Assessment of the technical state of elements and IEC is based on the analysis of the CTS failure risk, which combines the probabilities and possible damage from failures [3][4][5][6].…”

“…Assessment of the technical state of elements and IEC is based on the analysis of the CTS failure risk, which combines the probabilities and possible damage from failures [3][4][5][6]. The problems arising in assessing the risk of CTS failures are associated [2] with: a variety of equipment with structural and functional features, differing in physical principles and modes of operation, making it difficult to use universal methods, means of diagnosing the technical state of systems; а composition of numerous interconnected, interacting subsystems, blocks and nodes, with non-trivial IEC, between which there is a mutual exchange of energy, matter and information (EMI); incompleteness and vagueness of information, accounting complexity and interaction of interrelated subsystems and their elements.…”