Semantic Numeration Systems as Information Tools forFuzzy Data Processing
Vol. 12 No. 1 (2022): JITA – APEIRON Alexander Yu. Chunikhin, Vadym Zhytniuk Semantic Numeration Systems as Information Tools for Fuzzy Data Processing Original scientific paper DOI: https://doi.org/10.7251/JIT2201014C Download Article PDF Abstract We describe the concept of semantic numeration systems (SNS) as a certain class of context-based numeration methods. The main attention is paid to the key elements of semantic numeration systems – cardinal semantic operators. A classification of semantic numeration systems is given. The concept of fuzzy cardinal semantic transformation as a basis for creating fuzzy semantic numeration systems is advanced. Both fuzziness of the initial data – cardinals of abstract entities – and fuzziness of the parameters of the cardinal semantic operators are considered. The principle of formation of the fuzzy common carry in the cardinal semantic operators with multiple inputs is formulated Keywords: Cardinal Abstract Entity, Cardinal Semantic Operator, Semantic Numeration System, Fuzzy Cardinal Semantic Transformation. 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.
Train command and control for commuter and urban lines
Vol. 12 No. 1 (2022): JITA – APEIRON Efim Rozenberg, Alexey Ozerov Train command and control for commuter and urban lines Original scientific paper DOI: https://doi.org/10.7251/JIT2201005R Download Article PDF Abstract The paper presents the state of the art of command and control and the challenges faced by the Russian Railways (RZD), with a focus on the migration to new paradigms of train separation, train localization and obstacle detection. The authors give an overview of the practical results of some ongoing projects carried out with the direct involvement of NIIAS researchers and developers for the Moscow Central Circle (MCC) railway. Keywords: RZD; Virtual block; Moving block; Hybrid system; ATO; GOA3/4; Driver assistance system (DAS); Self-driving (autonomous) trains; Artifical Neural Network (ANN); GNSS; Digital route map. 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.
Evolution of Rail Operations Control Centres
Vol. 12 No. 2 (2022): JITA – APEIRON Efim Rozenberg, Alexey Ozerov Evolution of Rail Operations Control Centres Original scientific paper DOI:https://doi.org/10.7251/JIT2202166R Download Article PDF Abstract The article gives an overview of the evolution trends and stages of rail operations control centres (ROCCs) and outlines their future development and challenges in the context of the digital transformation of railway transport. It addresses the key aspects of further evolution of ROCCs in terms of closer integration of various functional layers and systems, automation of control and supervision functionalities, application of new data processing methods based on artificial intelligence. Keywords: Railway transport, digital transformation, Rail Operations Control Centres (ROCC), rail traffic control models, ERTMS, FRMCS, artificial intelligence, deep learning, Big Data. 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 Tree Data Structures for Systems Modeling
Vol. 12 No. 2 (2022): JITA – APEIRON Vasilyeva Marina Alekseevna, Filipchenko Konstantin Mikhailovich Application of Tree Data Structures for Systems Modeling Original scientific paper DOI:https://doi.org/10.7251/JIT2202152A Download Article PDF Abstract The authors developed a binary tree search library. The article presents UML diagrams of the developed classes. The authors supposed Abstract factory design pattern for the opportunity of using search trees’ node classes inheritance. The unit tests one developed. This library can be used in the creating the scheduling technical maintenance calculation automated system and the metro train energy optimal trajectory calculation system. Keywords: Tree structure, interval tree, transport system modeling. 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.
Research of Vulnerability Scanners of Web Applicationsof Intelligent Transport Systems
Vol. 12 No. 2 (2022): JITA – APEIRON Mikhalevich Igor Feodosevich, Fedorenko Bogdan Nikolaevich, Shelamov Maxim Dmitrievich Research of Vulnerability Scanners of Web Applications of Intelligent Transport Systems Original scientific paper DOI:https://doi.org/10.7251/JIT2202127F Download Article PDF Abstract The intellectualization of transport systems is accompanied by the widespread use of web applications. The paper presents a system of criteria for evaluating the effectiveness of vulnerability scanners for web applications of intelligent transport systems, the features of the functioning of which impose additional requirements for the secure development of applications used in critical information infrastructure and systems interacting with it. A study was made of the most famous web application vulnerability scanners. Keywords: Attack, computer attack, critical information infrastructure, information security, information security threat, intelligent transport system, vulnerability scanner, vulnerability web application. 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.
Methodology for calculating the probabilisticcharacteristics of the objectivity of the test results ofstudents in the distance learning system Moodle
Vol. 12 No. 2 (2022): JITA – APEIRON Lyzlov Sergey Sergeevich, Uvarov Sergey Sergeevich, Katina Marina Vladimirovna Methodology for calculating the probabilistic characteristics of the objectivity of the test results of students in the distance learning system Moodle Original scientific paper DOI:https://doi.org/10.7251/JIT2202111S Download Article PDF Abstract The article discusses the methodology for calculating the probabilistic characteristics of the objectivity of test results in distance learning. Calculation expressions are obtained and simulation modeling of the process of forming test tasks for students is carried out. The results of calculations and simulation modeling are given, estimates of a random discrete value are obtained, defined as the number of tests at which information about the content of all questions in test tasks becomes known to all students. Keywords:Moodle distance learning system, simulation modeling, calculation expressions and simulation results for assessing the probabilistic characteristics of the objectivity of student testing results. 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.
Methods and Principles of Construction of IntelligentUnmanned Systems for Train Control of Urban Off-StreetTransport
Vol. 12 No. 2 (2022): JITA – APEIRON Baranov Leonid Avramovich, Sidorenko Valentina Gennadievna, Balakina Ekaterina Petrovna, Loginova Lyudmila Nikolaevna Methods and Principles of Construction of Intelligent Unmanned Systems for Train Control of Urban Off-Street Transport Original scientific paper DOI:https://doi.org/10.7251/JIT22020100A Download Article PDF Abstract The principles of constructing intelligent unmanned traffic control systems for off-street urban rail transport are considered, while a block diagram and connections between subsystems are proposed. The features of the construction of upperlevel control algorithms are shown. Functional features of subsystems are defined, and links between subsystems are considered. Keywords: Urban Rail Transport System, Traffic Control, Control Algorithm, Unmanned Control, Intelligent Systems. 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.
Unmanned Aerial Vehicles Image Processing With the Useof a Neural Network
Vol. 12 No. 2 (2022): JITA – APEIRON Alekseev Viktor Mikhailovich, Khusenov Dodokhon Naimboevich, Andreev Andrey Andreevich, Chichkov Sergey Nikolaevich Unmanned Aerial Vehicles Image Processing With the Use of a Neural Network Original scientific paper DOI:https://doi.org/10.7251/JIT2202089M Download Article PDF Abstract Transport infrastructure facilities are critically important. To ensure their functioning, it is necessary to apply tracking methods that provide a high degree of protection. The article deals with the issues of unauthorized intrusion of foreign objects controlling, in order to prevent a dangerous impact on the infrastructure of high-speed transport. In this regard, it is proposed to conduct round-the-clock surveillance using unmanned aerial vehicles. Due to the fact that the range of UAV’s action distance is limited, therefore, it is proposed to use a remote method of detecting the intrusion of objects on the infrastructure with the use of an optical cable OK. The joint use of UAVs and OK allows to create a reliable system that provides control over the intrusion on the infrastructure. Special video cameras (thermal imagers, Lidar) are installed on unmanned aerial vehicles, providing inspection of the invasion area during day and night time. Since video recording devices have different resolution, the task is to apply methods for integrating data with different resolution and processing them by a neural network. The implementation of infrastructure tracking systems requires increasing demands on the network structure. One of the tasks set in this article is the development of the structure of the intrusion detection network on the high–speed ground transport infrastructure. Keywords: optical cable, local area computer network, structure of an unmanned vehicle network, video cameras, intrusion on infrastructure. 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.
Stages of Development, Methods and Intellectualizationof Automated Scheduling of Metro Passenger Trains
Vol. 12 No. 2 (2022): JITA – APEIRON Baranov Leonid Avramovich, Safronov Anton Igorevich, Sidorenko Valentina Gennadievna Stages of Development, Methods and Intellectualization of Automated Scheduling of Metro Passenger Trains Original scientific paper DOI:https://doi.org/10.7251/JIT2202077A Download Article PDF Abstract The article examines the underground passenger trains planned schedule automated construction system development stages. The technical means, basic methods and information technologies applied at system development stages aimed at its intellectualization, the ways of its integration into the Unified vehicles control automated traffic intelligent system on urban rail transport systems are described. Keywords: transportation scheduling, automation, autodriver, speech recognition, machine learning, artificial intelligence, uniformity, information technologies, intelligent transport systems, unified intelligent automated vehicle traffic control system. 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.
Rail Operations Control Centres
Vol. 12 No. 2 (2022): JITA – APEIRON Efim Rozenberg, Alexey Ozerov, Zoran Avramovich Rail Operations Control Centres Original scientific paper DOI:https://doi.org/10.7251/JIT2202069R Download Article PDF Abstract The article presents an overview of common trends in the evolution of rail operations control as well as the factors stipulating the existing approaches to the design of Rail Operations Control Centres (ROCCs) around the world. Based on the comparative analysis of various ROCCs and traffic parameters, the authors propose some classification of global traffic control models. The article outlines further steps towards a more detailed analysis of ROCCs in terms of their effectiveness by introducing a number of additional criteria and performance indicators to be taken into account. Keywords: Railway transport, Rail Operations Control Centres (ROCC), rail traffic control models, effectiveness, UIC. 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.