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A BP neural network incorporated regression forecast technique based upon fragment swarm optimization (PSO) is proposed to design the state of crucial components of wind turbine so regarding realize mistake identification and detection. Firstly, specification recognition is carried out on the collection and tracking information of the system, and parameters connected to fault detection are extracted. Then, the residual optimization issue is made use of to establish the forecast model of nonlinear state evaluation and semantic network combination, and the gearbox temperature level or generator bearing are input as criteria right into the semantic network combination model and single model specifically, and the precision of the design is mirrored by the examination index. Lastly, BP design and PSO-BP combined forecast model are developed respectively by using the actual operation data of wind ranch SCADA, and the mistake state is evaluated according to whether the anticipated residual exceeds the set threshold, so regarding keep an eye on the temperature level of wind turbine transmission and generator bearing. By contrasting the data videotaped prior to and after the failing and making the information prediction analysis, the speculative results show that the forecast model established in this paper is viable for the device element fault detection. [ABSTRACT FROM AUTHOR]

Abstract A BP neural network incorporated regression forecast technique based upon fragment swarm optimization (PSO) is proposed to design the state of crucial components of wind turbine so regarding realize mistake identification and detection. Firstly, specification recognition is carried out on the collection and tracking information of the system, and parameters connected to fault detection are extracted. Then, the residual optimization issue is made use of to establish the forecast model of nonlinear state evaluation and semantic network combination, and the gearbox temperature level or generator bearing are input as criteria right into the semantic network combination model and single model specifically, and the precision of the design is mirrored by the examination index. Lastly, BP design and PSO-BP combined forecast model are developed respectively by using the actual operation data of wind ranch SCADA, and the mistake state is evaluated according to whether the anticipated residual exceeds the set threshold, so regarding keep an eye on the temperature level of wind turbine transmission and generator bearing. By contrasting the data videotaped prior to and after the failing and making the information prediction analysis, the speculative results show that the forecast model established in this paper is viable for the device element fault detection.

With the rapid development of wind power industry, a large number of wind turbines are put into use. In order to make wind turbine operate efficiently and reduce the high maintenance cost due to faulty downtime, wind turbine fault prediction methods were researched. Based on the supervisory control and data acquisition( SCADA) data of a 2 000 kW doubly fed wind turbine for 14 months, a convolutional neural network-bidirectional long short-term memory(CNN-BiLSTM) algorithm model was established. Firstly, a convolutional neural network was used to reject the abnormal noise data in the input data, extract the key local features of the input data, and simplify the complexity of the input data. Then the processed input data was put into a bi-directional long and short-term memory network to perform active power prediction, and the active power prediction was performed in a bi-directional long and shortterm memory network. Finally, the processed input data was put into a two-way long-short time memory network for active power prediction, and the residual difference between the real and predicted values of active power was analyzed to complete the prediction of wind turbine faults. The results show that the constructed algorithm model has the stability of fault prediction, and can eliminate the misprediction caused by many factors, and make the fault prediction 4 days earlier than the SCADA system of the wind turbine, which provides a guarantee for avoiding the sudden shutdown due to the deterioration of the fault. [ABSTRACT FROM AUTHOR]

The object of this study is thinfilm photovoltaic modules without physical damage, manufactured by CIGS technology, affected by potentialinduced degradation, after restoration. The possibility of improving the efficiency of the array of restored photovoltaic modules of the Q.SMART UF L 105 type has been established experimentally, under natural lighting conditions, up to 50 % and higher with respect to the new reference photovoltaic panel. An expression for evaluating the efficiency of restored photovoltaic panels has been derived. It is proposed to use a relative indicator – the efficiency index, which is calculated based on the specific generation data of photovoltaic modules. During the experiment, photovoltaic panels were connected to the OpenSCADA dispatch control and data collection system. Experimental studies were carried out in the autumnwinter period under three weather scenarios: clear day, variable cloudiness, continuous cloudiness. The specific monthly generation of photovoltaic modules was defined as the amount of energy produced during the month per unit of power, which is 100 W. During the experiment, minute and hourly fluctuations in the generation of photovoltaic modules were recorded. Based on the results of calculating the efficiency index, it was concluded that the generation of electrical energy by exhausted photovoltaic modules after restoration under real operating conditions allows for the possibility of their secondary application. The results of the research could be used as a basis for evaluating the efficiency of restored photovoltaic modules while the resulting statistical data on their performance could be used to devise rules and standards for the secondary application of exhausted photovoltaic panels. [ABSTRACT FROM AUTHOR]

A lot of infrastructure upgrade and algorithms have been developed for the information technology driven smart grids over the past twenty years, especially with increasing interest in their system design and real-world implementation. Meanwhile, the study of detecting and preventing intruders in ubiquitous smart grids environment is spurred significantly by the possibility of access points on various communication equipment. As a result, there are no comprehensive security protocols in place preventing from a malicious attacker's accessing to smart grids components, which would enable the interaction of attackers and system operators through the power grid control system. Recently, dynamics of time-extended interactions are believed to be predicted and solved by reinforcement learning technology. As a descriptive advantage of the approach compared with other methods, it provides the opportunities of simultaneously modeling several human continuous interactions features for decision-making process, rather than specifying an individual agent's decision dynamics and requiring others to follow specific kinematic and dynamic limitations. In this way, a machine-mediated human-human interaction's result is determined by how control and physical systems are designed. Technically, it is possible to design dedicated human-in-the-loop societal control systems that are attack-resistant by using simulations that predict such results with preventive assessment and acceptable accuracy. It is important to have a reliable model of both the control and physical systems, as well as of human decision-making, to make reliable assumptions. This study presents such a method to develop these tools, which includes a model that simulates the attacks of a cyber-physical intruder on the system and the operator's defense, demonstrating the overall performance benefit of such framework designs. [ABSTRACT FROM AUTHOR]

The growing need for energy in emerging nations gives rise to apprehensions over energy security, so underscoring the criticality of harnessing the potential of renewable resources. Empirical evidence has demonstrated that photovoltaic systems integrated into the network are the optimal options for large-scale renewable energy. The analysis of the performance of these network-connected centrals can enhance the design, operation, and maintenance of new systems. The commissioning of a 12 MW photovoltaic solar power plant in Sidi Bel Abbès, located in the Dhaya region, has established it as one of the largest solar power plants in Algeria. The site yields a mean solar radiation of 5.21 kilowatt-hours per square metre per day and an average yearly temperature of 18.9 degrees Celsius. The present work provides an analysis of the yearly performance and conceptual aspects of the photovoltaic central unit. We compare the performance outcomes of the power plant with the simulated values generated by the SolarGis PvPlanner program. Analysis of real-world performance in relation to simulations allows for the detection of inconsistencies and the enhancement of future initiatives. SolarGis PvPlanner is an essential modelling program for accurately forecasting performance and effectively strategising solar projects, therefore guaranteeing sustainable and dependable energy for the future.

A lot of infrastructure upgrade and algorithms have been developed for the information technology driven smart grids over the past twenty years, especially with increasing interest in their system design and real-world implementation. Meanwhile, the study of detecting and preventing intruders in ubiquitous smart grids environment is spurred significantly by the possibility of access points on various communication equipment. As a result, there are no comprehensive security protocols in place preventing from a malicious attacker’s accessing to smart grids components, which would enable the interaction of attackers and system operators through the power grid control system. Recently, dynamics of time-extended interactions are believed to be predicted and solved by reinforcement learning technology. As a descriptive advantage of the approach compared with other methods, it provides the opportunities of simultaneously modeling several human continuous interactions features for decision-making process, rather than specifying an individual agent’s decision dynamics and requiring others to follow specific kinematic and dynamic limitations. In this way, a machine-mediated human-human interaction’s result is determined by how control and physical systems are designed. Technically, it is possible to design dedicated human-in-the-loop societal control systems that are attack-resistant by using simulations that predict such results with preventive assessment and acceptable accuracy. It is important to have a reliable model of both the control and physical systems, as well as of human decision-making, to make reliable assumptions. This study presents such a method to develop these tools, which includes a model that simulates the attacks of a cyber-physical intruder on the system and the operator’s defense, demonstrating the overall performance benefit of such framework designs.

Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0. [ABSTRACT FROM AUTHOR]

INTRODUCTION： With the continuous progress of science and technology, the monitoring and control of oil and gas pipeline networks have become more and more critical; SCADA systems, as a kind of technology widely used in industrial control, play a key role. The screen configuration of the SCADA system is the core part of its user interface, which is directly related to the operator's mastery of the status of the pipeline network. In order to improve the monitoring efficiency and reduce the operation risk, this study is devoted to exploring a new method of SCADA system screen configuration based on the oil and gas pipeline network model. PURPOSE: The purpose of this study is to develop an innovative SCADA system screen configuration method to present the operating status of the oil and gas pipeline network more intuitively and efficiently. The design based on the pipeline network model aims to enhance the operators' understanding of essential information, such as pipeline network topology, fluid flow, etc., so as to make monitoring and control more intelligent. METHODS: The study adopts a new method of SCADA system screen configuration based on the oil and gas pipeline network model. First, the topology, sensor data, and control nodes of the oil and gas pipeline network are comprehensively modelled. Then, through the design principle of human-computer interaction, the modelling results are integrated into the screen configuration of the SCADA system to realize the intuitive presentation of information. At the same time, advanced visualization technology is introduced so that the operators can understand the real-time changes in the pipe network status more clearly. RESULTS: After experimental verification, the new method shows significant advantages in oil and gas pipeline network monitoring. The operators can recognize the abnormalities of the pipeline network more quickly and accurately through the SCADA system screen configuration, which improves the efficiency of troubleshooting and treatment. The visualized interface design makes the operation more intuitive and reduces the possibility of operating errors, thus improving the safety and reliability of the pipeline network. CONCLUSION: The new method of SCADA system screen configuration based on the oil and gas pipeline network model has achieved significant results in improving monitoring efficiency and reducing operational risks. Through a more intuitive and intelligent interface design, operators can have a more comprehensive understanding of the operating status of the pipeline network, which provides practical support for rapid response and decision-making. This approach introduces new ideas to the field of oil and gas pipeline network monitoring, which is of positive significance for improving the overall performance of the system. Future work can be carried out to optimize the interface design further and expand the applicable scenarios.

This study highlights the importance of water infiltration in hydrological basin management, emphasizing its role in water services, water quality regulation, and temporal patterns. To measure this crucial function, this study introduces a portable and user-friendly tension infiltrometer designed for easy assembly and data collection. The tension infiltrometer, based on the 2009 design by Spongrová and Kechavarzi, offers a comprehensive characterization of the soil properties related to water flow. It eliminates the influence of preferential flow, providing accurate data. Additionally, it accommodates changes in pore size distribution within the soil, which is crucial for understanding water movement. This study discusses the challenges associated with traditional infiltration measurement tools, like double-ring infiltrometers and single rings, which are not easily transported and can lead to inaccuracies. In response, the proposed infiltrometer simplifies data collection, making it accessible to a broader range of users. This study also explores the use of the VL53L0X distance sensor in the infiltrometer, providing an innovative solution for measuring the water column height. The system's user interface allows real-time data collection and analysis, significantly reducing the processing time compared to that of the manual methods. Overall, this work demonstrates the potential for advancement in hydrological basin management using user-friendly instrumentation and automated data collection, paving the way for improved research and decision making in environmental services, conservation, and restoration efforts within these ecosystems. [ABSTRACT FROM AUTHOR]

Simple Summary: We provide a data classification and analysis method to estimate fire risk using facility data for thermal power plants. Experimental analysis is conducted on the data classified by the proposed method for 500 megawatt (MW) and 100 MW thermal power plants. In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states—Steady, Transient, and Anomaly—categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities. [ABSTRACT FROM AUTHOR]

A multi-purpose Universal Journal Bearing Test Rig (UJBTR) was designed and constructed for covering this research area. Design and construction of a control system for UJBTR has been introduced. It is a fully controlled system that helps obtain all the essential tribological values based on the conducted measurements and test trials. Further, it helps to verify the accurateness of the obtained values and outcomes. It consists of three control panels which ensure full control, monitoring and supervision of UJBTR via the Supervisory Control and Data Acquisition (SCADA) system. Utilizing UJBTR, it was possible to carry out different test trials for investigating the most critical operational factors affecting the performance of the lubricating oil film within journal bearings. Those factors involve the viscosity oil grade, the journal shaft speed and the applied load. Numerous test trials at different speeds and constant applied load were conducted to investigate the oil film pressure and temperature distribution within Circumferential Grooved Bearing (CGB). The accurate results obtained from the test trials assured the reliability of UJBTR control system and its ability to contain more extensive investigations and studies into the field of hydrodynamic lubrication within journal bearing.

Marine journal bearing is so often subject to the risk resulting from the loss of lubricating oil pressure, which negatively affects its performance. The inevitable result of such a condition is the complete failure of the main journal bearing of marine slow speed diesel engine. The study at hand introduces an experimental investigation aiming at better identifying the impact of oil supply pressure in relation to the pressure and temperature distribution within circumferential grooved bearing. Test trials have involved tracing those two critical profiles in case of optimal supply pressure as well as when it is either decreased or increased by 25%. The experimental procedures have also extended to cover the influence of two different oil grades on the outcomes derived for the previously mentioned operational conditions. All of the conducted experimental test trials were carried out with observation to the loading program of marine slow speed diesel engine involving different speeds and loads. It was concluded that maximum oil film pressure is seriously affected when oil supply pressure is decreased especially under high loads. In regard to the oil film temperature profile, it was observed that it is slightly affected under such operational conditions in which speeds are slow and range from 32 rpm up to 102 rpm.

In order to adapt to the management mode of the products pipeline network under the new system, set up a service-oriented pipeline system and make for the universality of batch tracking technology, a discussion was made about the products pipeline batch interface tracking technology under the PipeChina system. First of all, the oil batch coding rules and operation terms were standardized from the perspective of information level, which is favorable to the popularization and application. In terms of the development environment, it was considered an effective way to apply online real-time tracking to the accuracy improvement of products pipeline batch interface tracking software(hereinafter referred to as the tracking software), as well as the best way to develop the tracking software on the domestic SCADA system. In respect of technical scheme design, an interface tracking method with 50% concentration point(vol%) was determined and a design was made to the function and flow of tracking software. In response to the key challenges, the accuracy, calibration and error correction of batch tracking were analyzed and verified by the field application results. The customization and universality of the tracking software were discussed from three aspects: data system, oil transportation technology and model coefficient. As a result, an idea was proposed to design the tracking software interface from the software function level. Generally, the research results lay a foundation for the research on accurate products pipeline batch interface tracking technology and the opening of a new control mode of products pipeline network.

As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of the whole long-distance pipeline, gas transmission management, production scheduling, operation and maintenance coordination in production, and plays a very important role in the whole oil and gas pipeline. In this paper, firstly, the SCADA system for localized long-distance pipelines is explained in detail, including its basic structure and special solutions to problems. Secondly, the AdaBoost algorithm, which combines the MapReduce parallel computing framework, is introduced to collect and process data from the operation logs of the SCADA system and normalize the logs. Finally, to test the interaction between the improved AdaBoost algorithm and the SCADA system, a system test was conducted. The results show that the average latency of scheduling the logs of the SCADA system by AdaBoost algorithm with MapReduce parallel computing framework is only 39.82ms, the average processing speed of the log normalization file data under the multi-threaded mode of the system reaches 86.51GB/s, and the effective accuracy of the fault diagnosis is as high as 90.36%. This shows that the oil and gas pipeline SCADA system interacting with data visualization technology can process operation logs more quickly and can carry out real-time intelligent supervision of the working status and operation parameters of the whole auxiliary system, promoting the intelligent development of the oil and gas pipeline SCADA system.

AbstractWind energy has grown significantly over the last decade. With this, various improvements in the design of the wind turbine are geared towards increasing the reliability of several components. Wind turbulence has a huge effect on the fatigue loading of wind turbines considered in the design. Several monitoring methodologies, such as turbulence intensity analysis, are used to identify wind turbulence. In this paper, a method based on machine learning techniques and data from Supervisory Control and Data Acquisition (SCADA) systems is described. Five machine learning models are generated and compared in this study with the use of the operational data from the SCADA of wind turbines in a single wind farm. Results showed that the model based on Linear Regression in terms of a quadratic hyperparameter has lesser errors compared to the other models that were generated. Each parameter used in the creation of the model affects its performance. Observations in the nacelle system also showed higher errors due to the relationship between rotor speed and the blade angle. The rotor performance is mostly influenced by wind turbulence as the variation in wind speeds and rotational speeds have a certain correlation. Based on the results, it can be concluded that the use of SCADA data in generating turbulence models provides key insights into the relation of the turbulence intensity to the various components. It can be used as the basis for developing turbulence monitoring models that could help improve the design and operation of wind turbines.

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Queuing models are used to assess the functionality and aesthetics ofSCADAsystems for supervisory control and data collection. Here, the main emphasis is on how the queuing theory can be used in the system's design and analysis. The analysis's findings indicate that by using queuing models, cost-performance ratios close to the ideal might be attained. This article discusses a novelmethodology for evaluating the service-oriented survivability of SCADA systems. In order to evaluate the state of service performance and the system's overall resilience, the framework applies queuing theory to an analytical model. As a result, the SCADA process is translated using the MX/G/1 queuing model, and the queueing theory is used to evaluate this design's strategy. The supplemental variable technique solves the queuing problem that comes with the subsequent results. The queue size, server idle time, utilization, and probabilistic generating factors of the distinct operating strategies are estimated. Notable examples were examined via numerical analysis using mathematical software. Because it is used frequently and uses a statistical demarcation method, this tactic is completely acceptable. The graphical representation of this perspective offers a thorough analysis of the alleged limits. [ABSTRACT FROM AUTHOR]

A multi-purpose Universal Journal Bearing Test Rig (UJBTR) was designed and constructed for covering this research area. Design and construction of a control system for UJBTR has been introduced. It is a fully controlled system that helps obtain all the essential tribological values based on the conducted measurements and test trials. Further, it helps to verify the accurateness of the obtained values and outcomes. It consists of three control panels which ensure full control, monitoring and supervision of UJBTR via the Supervisory Control and Data Acquisition (SCADA) system. Utilizing UJBTR, it was possible to carry out different test trials for investigating the most critical operational factors affecting the performance of the lubricating oil film within journal bearings. Those factors involve the viscosity oil grade, the journal shaft speed and the applied load. Numerous test trials at different speeds and constant applied load were conducted to investigate the oil film pressure and temperature distribution within Circumferential Grooved Bearing (CGB). The accurate results obtained from the test trials assured the reliability of UJBTR control system and its ability to contain more extensive investigations and studies into the field of hydrodynamic lubrication within journal bearing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solar photovoltaic simulators are valuable tools for the design and evaluation of several components of photovoltaic systems. They can also be used for several purposes, such as educational objectives regarding operation principles, control strategies, efficiency, maintenance, and other aspects. This paper presents an automated solar photovoltaic simulation system with the capability to generate automated tests considering different parameters of solar photovoltaic panels and different operation conditions. The proposed simulator is composed of three buck-boost DC–DC power converters controlled in such a way that will behave similarly to solar photovoltaic panels. It allows to introduce additional variable loads and maximum power point tracker algorithms similar to real systems. Some converters are controlled by a DSP microcontroller connected to a single programmable logic controller which generates the automated tests. Thus, using the presented solution, it is possible to implement the I-V and P-V characteristic curves of solar photovoltaic panels and evaluate different maximum power point tracker algorithms considering different meteorological conditions and load variations, being a useful tool to teach subjects related to renewable energy sources and related applications. Several simulation results using Matlab/Simulink and experimental results are presented to validate the operation of the proposed solution. Experimental results achieve a ripple between 2% and 5% of the desired average current in MPP conditions. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An education system can be considered as an anthropotechnical system consisting of an education subject (e.g., trainee, operator), examiner (teacher), system decision maker (e.g., teaching module coordinator), and the environment (e.g., administrative, social, IT). The outcomes of this system are the knowledge and skills acquired by a trained student. The educational effect is usually diagnosed in the form of an exam or test. The study addresses the issue related to the credibility of applied assessment methods, knowledge qualification levels, and assessment errors. The analysis is conducted in accordance with the principles applied in statistical quality control when studying product population defectiveness. Using known methods for testing product defectiveness, the authors characterized the trainee educational effect (i.e., his/her knowledge and skill level) in a technical manner. The probability of an event wherein the examined person demonstrating a specific knowledge level achieves an adequate grade, has been adopted as the reliability measure. The conducted calculations provided graphs of the reliability functions involving the grades received within the "traditional" examination and testing process. The authors suggested an application that tests a SCADA system user, based on tools known to the trainee. The application was developed using the SCADA suite used as a visualization element in distributed control systems. This application enables the automation of the operator diagnosis process. [ABSTRACT FROM AUTHOR]

In an industrial world full of technological advancements where competitiveness is the essential objective, automation has become a fundamental necessity to achieve higher productivity with less chance of error in a limited time. Additionally, regular monitoring of processes is required to improve system performance and ensure employee safety. In this paper, a method for realizing an automated weighing and bagging machine is proposed, and special emphasis is placed on the weighing system. A simulated process prototype based on a SCADA (Supervisory Control and Data Acquisition) system and PLC (Programmable Logic Controller) of weighing and bag packing machines is designed to fill and close bags with the product. Operators can monitor the process and control outputs through the HMI (Human Machine Interface) screen. [ABSTRACT FROM AUTHOR]

Peer-to-peer network principles are the foundation of Blockchain Cybersecurity. Blockchain creates a reliable verification method that protects against online dangers. Cryptocurrency on the Blockchain is supported by three pillars: network availability, secrecy, and integrity. A third route toward stronger security, one that is less travelled and not nearly as inviting to attackers, is provided by Blockchain. This method lessens risks, offers robust encryption, and more successfully confirms the ownership and integrity of data. Some passwords, which are frequently referred to as the weakest link in Cybersecurity, may even be unnecessary without it. So we aim to build a secure user authentication system using blockchain and also learn about how SCADA systems work in healthcare. [ABSTRACT FROM AUTHOR]

In fact, attaining the final most beneficial target of improving the oil film lubrication for marine ‎‎purposes can be ‎said to have passed through several main stages up till now. The first stage ‎has ‎involved the initial design and ‎construction of the journal bearing test rig marked by ‎sufficient ‎validity for embracing the potential experimental ‎studies. Those studies were ‎concerned with ‎the lubricating oil within the journal bearing [1]. Notwithstanding, the ‎need ‎has then arisen for the ‎second stage which involved carrying out a comprehensive ‎modification and ‎conversion in ‎regard to the journal bearing test rig, aiming at enlarging its ‎capacity and range to endure more ‎‎sophisticated experiments. The modification has involved ‎adding a hydraulic loading system as ‎well as full ‎monitoring process via SCADA system, for ‎the sake of facilitating conducting all ‎potential experiments relating the ‎critical operating ‎factors that might otherwise affect the journal ‎bearing performance. This paper is representing the ‎‎second step in the long uninterrupted series of efforts seeking to ‎outline the results of a ‎proposed research program ‎for marine applications under the title of ‎‎ “Theoretical and ‎‎Experimental Analysis of the Performance of Journal ‎Bearing”. ‎ The program ‎will deal with ‎‎different design and construction aspects of journal bearing, with special ‎‎reference ‎to marine ‎applications. ‎

In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states—Steady, Transient, and Anomaly—categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities.

This study highlights the importance of water infiltration in hydrological basin management, emphasizing its role in water services, water quality regulation, and temporal patterns. To measure this crucial function, this study introduces a portable and user-friendly tension infiltrometer designed for easy assembly and data collection. The tension infiltrometer, based on the 2009 design by Spongrová and Kechavarzi, offers a comprehensive characterization of the soil properties related to water flow. It eliminates the influence of preferential flow, providing accurate data. Additionally, it accommodates changes in pore size distribution within the soil, which is crucial for understanding water movement. This study discusses the challenges associated with traditional infiltration measurement tools, like double-ring infiltrometers and single rings, which are not easily transported and can lead to inaccuracies. In response, the proposed infiltrometer simplifies data collection, making it accessible to a broader range of users. This study also explores the use of the VL53L0X distance sensor in the infiltrometer, providing an innovative solution for measuring the water column height. The system’s user interface allows real-time data collection and analysis, significantly reducing the processing time compared to that of the manual methods. Overall, this work demonstrates the potential for advancement in hydrological basin management using user-friendly instrumentation and automated data collection, paving the way for improved research and decision making in environmental services, conservation, and restoration efforts within these ecosystems.

This article consists of the development of a new project carried out by a leading national company which was innovating the way to optimize its production costs and improve its processes to achieve its annual goals. This improvement began with the project to use the energy generated in its facilities. For the abovementioned company, having implemented an industrial network and a human-machine interface that allows it to acquire and quantify data on the plant's energy efficiency is the basis of its electric power generation process. Part of this electricity is fed into the public grid, so this project aims to implement a system that provides information to the control center of the electricity distribution company through a wireless communication link in order to send all the signals required by the entity responsible for energy distribution so that it can supervise and monitor them properly as stipulated by law with respect to electricity generation companies, thus ensuring compliance with all the legality of the case and finally having a successful implementation. [ABSTRACT FROM AUTHOR]

Belt conveyors are the most common type of continuous transport machines in all industries. They are usually not only integral part of technological process, but also determine its pace, rhythm, significantly affect the organization of all production. At the enterprises of mining and metallurgical complex, belt conveyors are used at about 90 % of total amount of conveyors, so the issue of monitoring and diagnosing the condition of conveyor equipment in real time is very important task. The most promising in this regard is creation of system for monitoring of conveyor equipment technical condition with visualization of basic parameters based on modern solutions from companies such as Schneider Electric or Siemens, which provide control and monitoring of all parameters and allow the operator to track all changes in conveyor equipment. The article offers one of options for creating such system. Since main components of conveyor equipment are induction motor and rollers, it is control of their technical condition that comes to the fore. Vibrodiagnostics, noise measurement and thermal imaging methods are used for this purpose, and the SCADA system will allow their combination and visual reproduction. The basis of this system is diagnostic models of the main mechanisms, which consisted of five levels: the main components and parts that are most vulnerable and diagnosable; basic structural parameters that determine reliability; characteristic breakages and defects of knots; characteristic changes of diagnostic signals and diagnostic signs and methods of diagnosis.

The article analyzes the system of automated process control at the elevator to determine promising areas for improving the quality of grain. The structural scheme of control and the algorithm of laying the transport route of grain according to the specified coordinates on the basis of SCADA-system are investigated. One of the directions of increase and improvement of technological process on the elevator is defined, which consists in improvement of control algorithms in the direction of optimization of technological processes of the elevator on many criteria (minimum of energy consumption, minimum of grain fighting, minimum of transport time), product performance

Highly notable cyber-attacks, such as Stuxnet [1] and the Maroochy attack [3], have targeted critical infrastructure to affect physical processes to cause harm. This work presents a payload analysis-based Intrusion Prevention System (IPS) to detect similar attacks by predicting what harm the attacks could cause to the physical process. The IPS developed is called the Embedded Process Prediction Intrusion Prevention System (EPPIPS). EPPIPS examines incoming command packets and ladder logic programs that are destined for a Programmable Logic Controller (PLC) that interacts with a physical process. If EPPIPS predicts these packets or programs to be harmful, EPPIPS can potentially prevent or limit the harm. EPPIPS resides inside the PLC itself as a proxy process between the actual PLC process and the network. The purpose of this approach is to serve as the innermost layer of defense relative to the PLC for cyber-attacks in a defense in depth strategy. The metrics used when evaluating the results in this work included latency and the accuracy of the predictions. [ABSTRACT FROM AUTHOR]

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