On Intrusion Detection in a Neighbourhood Area Network in the Smart Grid
Vol. 2 No. 1 (2012): JITA – APEIRON Nasim Beigi-Mohammadi, Hamzeh Khazaei, Jelena Mišić, Vojislav B. Mišić On Intrusion Detection in a Neighbourhood Area Network in the Smart Grid Original scientific paper DOI: https://doi.org/10.7251/JIT1201007M Download Article PDF Abstract Smart grid, which is an upgrade of power electric system, mainly relies on powerful communication networks to provide a secure, reliable and efficient information delivery. Updating a system as complex as the electrical power grid with a large number of components has the potential of introducing new security vulnerabilities into the system. Hence, security mechanisms should be deployed to protect the smart grid as a first wall of defence against malicious attacks. As a second wall of defence, there should be intrusion detection systems in place to protect the smart grid against any security breaches. In this work, we describe an anomaly-based intrusion detection system (IDS) for neighbourhood area network whose security is of critical importance in smart grid. Keywords: Intrusion Detection System, smart grid, Neighbourhood Area Network Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Management of Induction (Asynchronous) Motors Using PLC
Vol. 2 No. 2 (2012): JITA – APEIRON Mahir Zajmović, Hadzib Salkić, Saša Stanić Management of Induction (Asynchronous) Motors Using PLC Original scientific paper DOI: https://doi.org/10.7251/JIT1202095Z Download Article PDF Abstract This paper describes the management of an induction (asynchronous) motor using PLC and VSD. For the realization of the practical part of this paper Schneider Electric equipment was used, which makes a complete system that is used in Natron Hayat d.o.o Maglaj, where this experiment was done. For this paper, a Schneider Zelio PLC was used, which with the aid of a 5.5 kW Schneider Altivar ATV312HU55N4 modulator (frequency transformer), managed the work of a 5.5 kW induction motor at speed of 1500 RPM. Managing controls were given for HP mobile working stations, where Windows XP operating system with SCADA software from DAQFactory was installed. The link used between the working stations and the PLC was Ethernet (Modbus TCP/IP). Keywords: asynchronous, motor, PLC, VSD, frequency, transformer, management, SCADA, software, DAQFactory. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Contemporary Java Web Technologies as a Service for the University Employees
Vol. 2 No. 2 (2012): JITA – APEIRON Uroš Romić, Igor Manić, Ivan Pantelić Contemporary Java Web Technologies as a Service for the University Employees Original scientific paper DOI: https://doi.org/10.7251/JIT1202088R Download Article PDF Abstract This paper describes the web application used by employees of the School of Electrical Engineering in Belgrade. The application is based on contemporary open source Java web technologies (including frameworks such as Spring, JSF, Hibernate, etc.). They were combined together into an advanced system that provides an environment for the rapid application development, high modularity and configurability. Paper describes main groups of application functionalities related to teaching process and financial operations, as well as additional functionalities. Application three-tier architecture is described in detail, with the description of technologies used in each tier. Application development environment is presented including build process management. Also, the security solution is described, as well as distributed computing model chosen for communication with other information systems within the School of Electrical Engineering. Keywords: Java, Spring, JSF, Distributed Computing. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Data Mining and Cloud Computing
Vol. 2 No. 2 (2012): JITA – APEIRON Robert Vrbić Data Mining and Cloud Computing Original scientific paper DOI: https://doi.org/10.7251/JIT1202075V Download Article PDF Abstract Cloud computing provides a powerful, scalable and flexible infrastructure into which one can integrate, previously known, techniques and methods of Data Mining. The result of such integration should be strong and capacitive platform that will be able to deal with the increasing production of data, or that will create the conditions for the efficient mining of massive amounts of data from various data warehouses with the aim of creating (useful) information or the production of new knowledge. This paper discusses such technology – the technology of big data mining, known as Cloud Data Mining (CDM). Keywords: data mining, cloud computing, cloud data mining, NoSQL. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Data Visualization on Information Tables – Dashboards
Vol. 2 No. 2 (2012): JITA – APEIRON Nedim Smailović Data Visualization on Information Tables – Dashboards Original scientific paper DOI: https://doi.org/10.7251/JIT1202068S Download Article PDF Abstract Today’s level of the information technology development allows gathering of large amount of data relevant for a company. The issue of way and method of data gathering has been solved, however, at the same time the need also arises for extraction of the most important ones, since the quantity of the input data itself is not enough. In the contemporary business activities, being pursued in the circumstances of high competition, changes, speed and risk, management of the company may be compared to driving a fast car. Therefore, the information tables – dashboards have been created for the purpose of data processing as an analogy to the car dashboard, which, by means of several indicators, provides for the driver an instant insight into various data related to driving and the engine running. Display of the data in the form of charts and diagrams undoubtedly helps in obtaining new knowledge however, an individual visual displaying is seldom sufficient thus they should be combined in different variants. The work presents concrete examples of the dashboards creation both locally and globally. Keywords: Dashboard, information tables, business inteligence (BI), visual language, charts and diagrams. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Application of Voice Biometrics in Protection Systems and Crime Fighting
Vol. 2 No. 2 (2012): JITA – APEIRON Saša Paunović, Lazar Nešić, Jovan Kovačević Application of Voice Biometrics in Protection Systems and Crime Fighting Original scientific paper DOI: https://doi.org/10.7251/JIT1202059P Download Article PDF Abstract Modern communication relies increasingly more on the verbal communication between a machine and a human, aiming to govern certain resources and robots, increase the security of certain means, initiate certain processing protocols, faster financial transactions… This paper illustrates the possibility of using the voice biometrics in modern living, from simple examples, such as starting the motor of a vehicle, through opening security gates, to proving fraud and embezzlement. Special emphasis has been put on the systems of automatic speaker identification and forensic speaker recognition. Keywords: Biometry, Biometry systems, Speech, Speaker, Voice recognition, Identification, Security. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Software Simulations Usage in Business Decision Making Education
Vol. 3 No. 1 (2013): JITA – APEIRON Marko Marković, Katarina Plečić Software Simulations Usage in Business Decision Making Education Original scientific paper DOI: https://doi.org/10.7251/JIT1301051M Download Article PDF Abstract Because of great importance in improving business decision making teaching process in educational institutions, a large number of software simulators are developed. Based on that information, it was necessary to present simulations as one of the most modern educational solutions, with possibilities of their usage. The basic features of a software system developed to support the teaching of business decision making and machine learning algorithms used in this field at the the Singidunum University Faculty of Business Valjevo, have been presented in the paper. Keywords: software business simulations, business decision making, machine learning. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
The innovation ICT strategy in agri-food sector
Vol. 3 No. 1 (2013): JITA – APEIRON Luisa Sturiale, Alessandro Scuderi The innovation ICT strategy in agri-food sector Original scientific paper DOI: https://doi.org/10.7251/JIT1301043S Download Article PDF Abstract The achievement of Information Communication Technology (ICT) as a new ground for economic competition is deeply affecting the trade organization in many merchant sectors. For Italian agri-food products it is of absolute importance for Internet marketing to be undertaken and to foresee the consequent scenarios. The aim of this research is to exactly assess the opportunities and problems of the distribution circuit based on the virtual scenario, with a methodological and empirical approach, working on the analysis of experiences already begun by agri-food companies established in Italy and engaged in “business to consumer” and “business to business”. The ICT is configured as a phenomenon in a continuous and rapid evolution, which makes it necessary for companies to continually adapt to it and to the habits of web-consumers. This means that it is necessary to effectively enter the network of agri-food firms, and to strategically revise marketing methods focusing on the market place. Keywords: web marketing, e-business, agri-food , web site. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
A Case Study on Introducing E-learning into Seafarers’ Education
Vol. 3 No. 1 (2013): JITA – APEIRON Sanja Bauk, Michael Kopp, Zoran Avramović A Case Study on Introducing E-learning into Seafarers’ Education Original scientific paper DOI: https://doi.org/10.7251/JIT1301034B Download Article PDF Abstract This paper considers beginning steps in introducing e-learning into seafarers’ education, as additional mode of acquiring knowledge at the Faculty of Maritime Studies which is a part of the University of Montenegro. Related activities are the result of the enthusiasm of few professors and they are partly supported by a small, initial project of bilateral scientific and technological cooperation between Austria and Montenegro. The paper is conceived in a way that it considers following issues: (a) a brief discussion of some current shortages in maritime education and training in general; (b) possibilities of getting advantages through introducing e-learning into this respectable field of education; (c) some advantages and disadvantages of Moodle which has been used as a technological platform for introducing e-learning in the analyzed case; (d) results of the surveys conducted among involved students, teachers, and professionals in the field of employing new media techniques into the knowledge transfer, and (e) some conclusion remarks regarding possibilities of optimal combining maritime and virtual education. Keywords: seafarers’ education, e-learning, surveys’ analysis. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Using Kerberos protocol for Single Sign-On in Identity Management Systems
Vol. 3 No. 1 (2013): JITA – APEIRON Ivan Milenković, Olja Latinović, Dejan Simić Using Kerberos protocol for Single Sign-On in Identity Management Systems Original scientific paper DOI: https://doi.org/10.7251/JIT1301020E Download Article PDF Abstract Today, identity management systems are widely used in different types of organizations, from academic and government institutions to large enterprises. An important feature of identity management systems is the Single Sign-On functionality. Single Sign-On allows users to authenticate once, and freely use all services and resources available to them afterwards. In this paper, we present the usage of Kerberos in identity management systems. An overview of Kerberos protocol, state of the art of identity management systems and different generic architectures for identity management is given in the paper. Also, we present a Single Sign-On identity management architecture proposal based on Kerberos protocol, and discuss its properties. Special attention was given to authentication, authorization and auditing. Keywords: identity management, authentication, Kerberos, Single Sign-On. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.