Maintenance Costs Research Articles

Дистанционная калибровка динамических вагонных весов

Metrological maintenance of measuring instruments is aimed at ensuring the uniformity of measurements. It allows you to achieve the required accuracy of the measured physical quantities, determine corrections to the readings of measuring instruments and estimate the error. With the development of scientific and technological progress, the achievement of accuracy is ensured by more advanced and simple methods. Remote calibration of dynamic wagon scales examines the current issues of the application of remote calibration methods in the field of measurements in transport. The analysis of the advantages of remote calibration in comparison with traditional metrological services emphasizes economic efficiency and reduces time costs. The article describes the measurement process using remote control methods, the use of which will allow the introduction of remote calibration of dynamic wagon scales in the railway industry. Purpose: to describe the possibility of remote calibration of dynamic wagon scales. Methods: conducting an experiment of remote calibration of dynamic wagon scales using the global Internet and special software, a weighing program and communication programs. Results: the possibility of remote calibration of dynamic wagon scales was determined, the necessary programs for operation were described, advantages and disadvantages were presented. Practical importance: the possibility of introducing remote calibration of dynamic wagon scales has been determined, which will reduce personnel time and financial costs for metrological maintenance and periodic calibration.

REMAINING USEFUL LIFE PREDICTOR FOR EV BATTERIES USING MACHINE LEARNING

Electric vehicles (EVs) are a key solution to combat rising carbon emissions and reduce dependence on fossil fuels. In India, the government has implemented policies such as the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) scheme to promote EV adoption Predicting the Remaining Useful Life (RUL) of EV batteries using machine learning ensures better battery health management and enhances operational efficiency. Applications include EV fleet management, battery recycling, and cost-effective maintenance. To develop a machine learning model that accurately predicts the Remaining Useful Life (RUL) of EV batteries to improve operational reliability, reduce maintenance costs, and support sustainable energy practices. Before the advent of machine learning, traditional methods for estimating EV battery life relied on rule-based approaches, where predefined thresholds such as voltage drops or charge cycles were used to predict battery health. Empirical models, often linear, were developed based on historical performance data but lacked the ability to adapt to dynamic usage patterns. Additionally, manual battery testing was a common practice to measure degradation, though it was time-consuming, labor-intensive, and often prone to inaccuracies in capturing the complex nature of battery aging. Traditional systems for predicting EV battery life are largely empirical and rely on static models that fail to capture dynamic battery behavior. These methods often lack precision, are labor-intensive, and provide limited adaptability to varying usage conditions, leading to inefficiencies in battery management. The increasing demand for EVs and their critical dependence on battery performance drives the need for accurate RUL prediction systems. Traditional methods are insufficient in addressing the complex, non-linear nature of battery degradation. The proposed machine learning-based system leverages real-time battery performance data, including metrics like voltage, current, temperature, and charge-discharge cycles, to train predictive models capable of estimating the Remaining Useful Life (RUL) of EV batteries. This approach significantly enhances accuracy by capturing complex patterns in battery degradation, enables real-time predictions for immediate insights, optimizes costs by minimizing unnecessary replacements and maximizing resource utilization, and promotes sustainability through efficient recycling and reduced battery waste.

Predictive Maintenance for UWSNs: A Practical Implementation of Fault Detection Using NDN and Deep Learning

ABSTRACTUnderwater Wireless Sensor Networks (UWSNs) play a critical role in collecting data for various marine applications, but their deployment and maintenance in harsh underwater environments pose significant challenges. Building upon our prior conceptual work, this article presents a practical implementation of an AI‐driven fault detection system for UWSNs, a key component of a future predictive maintenance framework incorporating Named Data Networking (NDN). Using the “Underwater Sensor Dataset,” we implemented a feed‐forward neural network to classify sensor readings as healthy or faulty. The model achieved high accuracy (99.9%), precision (100%), recall (99.1%), and F1‐score (99.6%) on the test set, demonstrating its effectiveness in fault detection. Our findings highlight the feasibility and potential of AI‐based predictive maintenance in UWSNs, even with a simpler neural network model. The framework promises reduced downtime, minimized maintenance costs, and an extended lifespan for UWSNs.

Temperature and Humidity Monitoring using NodeMCU

The paper thus proposes an advanced system that monitors the temperature at various points of location in a data centre and makes this temperature data visible over the internet through cloud-based dashboard and sends SMS and email alerts to predefined recipients when temperature rises above safe operating zone and reaches certain high values. Most of our daily life activities depend on environmental conditions. Traditionally, such parameters are monitored inefficiently with a wired moni toring system, which incurs greater cost of implementation and maintenance. Moreover, the device to detect temperature in terms of a thermometer is not at all appropriate for online measurement because it requires more time to give a response in terms of measurement. The said technology facilitates remote, efficient monitoring of temperature as well as humidity. This system captures real-time data about temperature and humidity in rooms, which ensures safety, protection of equipment, and quality control. The two main parts of this system include a nodeMCU microcontroller equipped with ESP8266 Wi-Fi capability to easily send data to the Arduino IoT Cloud. In this system, DHT11 is applied in determining environmental conditions

EFFECTS OF ASYMMETRICAL STATOR STRUCTURE ON BLDC MOTOR PERFORMANCE FOR HOUSEHOLD APPLIANCES

Brushless direct current motors (BLDCM) have been frequently preferred in the industry, especially in home appliances, due to their advantages such as long life, high efficiency, low maintenance costs and ease of control. In addition to these advantages, the main disadvantages of these motors are known to be high torque ripple and vibration levels. In this study, alternative asymmetric designs are realized in the stator tooth structure of the BLDCM designed for home appliances, and comparative analyses of the effects of the non-uniform air gap caused by the asymmetric tooth structure on the motor performance are presented. Two different asymmetric structures, namely the model with clockwise asymmetric design (CW) and the model with counterclockwise asymmetric design (CCW) in the stator tooth structure, are determined in the stator design. With these alternative designs, improvements are made in terms of torque ripple and cogging torque when compared with the reference motor; notably, the CCW-2 model achieves the lowest ripple at 19.4% compared to 23.1% for the reference motor, and a 42% reduction in cogging torque. The design of the motor models is made in ANSYS EDT program as two dimensional (2D) and analyzed with the Finite Element Method (FEM). In the analysis results, air gap flux density, torque, torque ripple and cogging torque are examined, and the flux density and torque spectrum obtained by Fast Fourier Transform (FFT) are presented comparatively

ROLE OF GREEN BUILDINGS IN THE SUSTAINABLE DEVELOPMENT OF TIER-II CITIES IN INDIA

Green building technologies (GBTs) have advanced significantly, as a result of the advantages to society, economy, and environment. It might lead to more environmentally friendly growth, considering climate change. The primary goal of GBTs is to utilize water, energy, and other resources sparingly in a balanced manner. Green buildings (GBs) have advantages in terms of reduced energy use and emissions, low operating and maintenance costs, improved productivity, and health. To determine the future course for sustainable green building technologies, a critical analysis of current residential projects in the field is mandatory. Conducting research that pertain to sustainable construction practices minimizing natural resources, are reasonably priced, are planned and built with the possibility of future growth. This article looks at how green building construction is going on, based on case study and offers suggestions for more research and development that will be required for residential projects leading to a sustainable future.

MATHEMATICAL MODEL OF THE MOVEMENT OF TABLE BEET ROOT CROP ALONG THE SPIRALS OF A VIBRATING CLEANER

A pressing issue in the technological process of table beet production is the cleaning of root crops from adhered soil, loose impurities, and plant residues after harvesting. The efficiency of this process significantly determines the quality of the final product, its suitability for long-term storage, and subsequent processing. Modern root crop cleaning technologies widely use mechanical methods, among which vibrational and spiral cleaning systems hold a special place. However, many existing designs have several shortcomings, such as insufficient impurity removal efficiency or an increased risk of root crop damage. This study presents a new design of a vibrational spiral cleaner developed to enhance the efficiency of table beet root cleaning. A mathematical model of the root crop's movement along the working channel of the cleaner was constructed to analyze and optimize the operation of this design. It is during this movement that adhered soil, loose impurities, and plant residues are removed. Based on the developed equivalent scheme, a system of differential equations describing the dynamics of the root crop's movement along the surface of the working channel was formulated. Numerical solutions of this system on a personal computer made it possible to study the influence of structural parameters (spiral geometry, pitch, and material) and kinematic parameters (vibration frequency and amplitude) on the speed of root crop movement and the cleaning quality. The analysis of the obtained results showed that optimizing the cleaner's parameters ensures uniform cleaning, reduces the likelihood of root crop damage, and increases the overall process efficiency. The results of this research have practical significance for the further improvement of cleaner designs and their implementation in industrial technologies. The application of the proposed technical solutions will contribute to improving the cleaning quality of table beet root crops, enhancing production efficiency, and reducing equipment maintenance costs.

ANALISIS LIFE CYCLE COST PADA GEDUNG KANTOR BPVP KOTA SORONG

The construction of a building construction must be planned and taken into account the estimated costs that will occur. One of the calculation methods that can be used is the Life Cycle Cost (LCC) method. Life Cycle Cost (LCC) is the cost required by a building during the life of the building plan. Included in these costs are planning and construction costs called initial costs, operating costs, maintenance costs, and demolition costs. In this case, the building under review is the office building of the Balai Pelatihan Vokasi Dan Produktivitas (BPVP) city of Sorong which has undergone renovations and needs to be taken into account for Life Cycle Cost. The purpose of this research is to calculate the cost of Life Cycle Cost (LCC) based on the amount of routine maintenance and periodic maintenance costs. Through the calculation process, four components are obtained that underlie the Life Cycle Cost, namely the initial cost or construction cost of Rp.4.703.149.690,60, the operational cost of Rp.128.166.180.245,80, the maintenance cost of Rp.15.698.538.615,10, and the demolition cost of Rp.1.504.632.833. Keywords: life cycle cost,service life,maintenance cost,the economic life of the building.

Socioeconomic assessment of investments in dental anthropomorphic robots for practical training of dentistry students

BACKGROUND: Robotics is a rapidly evolving area, especially in medical education, where robots improve the results of training and professional qualification of physicians. Dental anthropomorphic robot (DARs) are valuable tools that assist dentistry students in acquiring practical skills, expanding theoretical knowledge, and improving nonverbal communication skills. Given the current practical training issues, the use of such robots becomes increasingly relevant. However, high production and implementation costs require a detailed economic evaluation to assess the practicality of such devices. AIM: To perform a socioeconomic assessment of investments in DARs for practical training of dentistry students. MATERIALS AND METHODS: A socioeconomic assessment of the use of DARs for practical training of dentistry students at a medical college was performed. The assessment focused on the cost-benefit analysis of implementing DARs for practical training. Cost-effectiveness was assessed using the net present value (NPV), profitability index (PI), internal rate of return (IRR), and payoff period. The data were acquired from the Dentistry training program of the Russian University of Medicine. The data included training costs, robot purchase and maintenance costs, and organization of studies. RESULTS: The use of one robot per 10 students results in a positive NPV of 54,279,963 rubles, with a PI of 5.52, an IRR of 70.79%, and a payoff period of approximately one and two years, respectively. These findings confirm the economic efficiency of the project. When using one robot per one student, the values are negative, indicating the economic inefficiency of this scenario. CONCLUSION: The study showed that the use of DARs for practical training of dentistry students results in a high economic efficiency. High NPV, PI, and IRR values confirm the profitability of the project. Moreover, the robot improves practical skills by increasing the accuracy and speed of dental procedures.

Preliminary design and evaluation of a ducted-fan type pipeline robot

Pipelines are crucial infrastructure supporting human life, yet their maintenance costs are substantial, necessitating the development of time-efficient robotic systems. Therefore, this study proposes an in-pipe mobile robot powered by wind generated using a ducted fan rotor, enabling faster movement within pipelines than conventional methods, e.g., inchworm. Deriving the propulsion force from wind requires analyzing airflow within the pipeline, which is challenging due to the confined space and complexity, especially when considering the presence of robots. Hence, we developed a prototype of a ducted-fan type pipeline robot (DPR) and experimentally investigated duct shapes that enhance the propulsion of the DPR within pipelines. As a result, we identified duct shapes that amplify the propulsion force generated by the ducted fan within pipelines. Additionally, we also experimentally elucidated the relationship between the distance from the pipe’s end and the propulsion force of the robot. Furthermore, we demonstrated the capabilities of the DPR by traversing a pipeline with a diameter of 110 mm, achieving speeds of 1500 mm/s in horizontal pipes and 700 mm/s while ascending vertical pipes. The results show that DRP has the potential for in-pipe inspection.

DEFECT SEVERITY CODE PREDICTION BASED ON ENSEMBLE LEARNING

In machine learning, learning algorithms that learn from other algorithms are called meta-learning. New algorithms called Ensemble algorithms have surfaced as a viable method to improve defect prediction models' accuracy and dependability. In software development defect prediction of software engineering is still a big challenge, and leads to the failure of systems, increases the cost of maintenance, and makes the development process more difficult. Consequently, defect prediction systems have become more popular as a way to foresee possible flaws early on in the development process. Defect prediction is the process that specifies the possible defects in the code written newly or the existing modified code without the use of code testing. This paper introduces ensemble learning ideas, reviews the traditional defect prediction models, and investigates ensemble learning techniques for defect classification and prediction such as bagging, boosting, stacking, and random forests, Case studies and actual experiments illustrate the important role of ensemble algorithms in classifying five severity types of defects and predicting the severity code of defects to improve the software development process by reducing the time and effort needed to determine the type of defect.

Adaptive Neuro-Fuzzy System for Detection of Wind Turbine Blade Defects

Wind turbines are the most frequently used objects of renewable energy today. However, issues that arise during their operation can greatly affect their effectiveness. Blade erosion, cracks, and other defects can slash turbine performance while also forcing maintenance costs to soar. Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. The goal of this study is to develop the improved adaptive neuro-fuzzy inference system (ANFIS) for wind turbine defect detection, which will reduce computing resources and increase its accuracy. Unmanned aerial vehicles are deployed to photograph the turbines, and these images are beamed back and processed for early defect detection. The proposed adaptive neuro-fuzzy inference system processes the data vectors with lower complexity and higher accuracy. For this purpose, the authors explored grid partitioning and subtractive clustering methods and selected the last one because it uses three rules only for fault detection, ensuring low computational costs and enabling the discovery of wind turbine defects quickly and efficiently. Moreover, the proposed ANFIS is implemented in a controller, which has an accuracy of 91%, that is 1.4 higher than the accuracy of the existing similar controller.

Hydraulic fault detection of wind turbine generators using artificial neural networks

In the current context where fossil resources are diminishing globally, and carbon emissions are increasing daily, the importance of green energy, particularly wind energy, is growing significantly. The increasing of wind turbines will not only reduce the carbon footprint but also decrease dependence on external resources. To increase the installed capacity of wind turbines, it is crucial to reduce not only installation costs but also operational costs. The largest proportion of operational costs is service, and maintenance costs. One of the most critical approaches to reducing service, and maintenance costs is preventive maintenance activities. The objective of preventive maintenance activities is to minimize or ideally eliminate production losses through scheduled turbine shutdowns before failures occur. In this study, artificial neural network-based algorithms that predict potential hydraulic failures during the operational period were utilized. For this purpose, data from the turbine SCADA system over a period of two years, considering the equipment, and sensors connected to hydraulic systems, were compiled. The study was conducted using the WEKA program, comparing Multilayer Perceptron (MLP), Radial Basis Function Classifier (RBF Classifier), SMOreg (Support Vector Machines for Regression) algorithms. Result of the study, the MLP algorithm was applied with a percentage split of 66% for training, and 33% for testing, achieving a prediction accuracy of 96.32%

Evaluation of water hardness treatment methods using sodium hydroxide and their impact on water quality

Hard water is an environmental and technical challenge caused by high concentrations of calcium (Ca²⁺) and magnesium (Mg²⁺) ions, leading to scale formation that affects the efficiency of household and industrial appliances and increases maintenance and treatment costs. Additionally, hard water can pose health risks and reduce its quality for daily use. This study aims to evaluate the effectiveness of different doses of sodium hydroxide (NaOH) in treating water hardness and improving water quality by reducing total hardness (TH) and adjusting the physicochemical properties of water. Three main dosages were tested: the first dosage (5 g/L of NaOH), which assessed the basic effectiveness of sodium hydroxide; the second dosage (2.5 g/L of NaOH and 2.5 g/L of Na₂CO₃ with HCl and acid), aimed at balancing hardness reduction with mineral retention; and the optimized dosages (10, 20, and 30 mg/L of NaOH), which proved to be the most effective and balanced. The optimized doses successfully reduced total hardness to levels compliant with Algerian standards while maintaining calcium and magnesium concentrations within acceptable ranges. Furthermore, electrical conductivity (EC) remained within safe limits without significant increases, ensuring the water did not become oversaturated with dissolved salts. The slight increase in sodium (Na⁺) concentration also remained within permissible levels, enhancing the water's suitability for drinking and other uses. Based on these results, the optimized NaOH dosages are the optimal solution for water hardness treatment, achieving a balance between reducing hardness and ensuring water quality in compliance with local standards.

Greater adherence to the Mediterranean diet pattern in the United States is associated with sustainability trade-offs

BackgroundThe Mediterranean diet pattern has been consistently associated with health benefits but less is known about the association with environmental and economic sustainability in the United States (US). This information is needed to support sustainable policy agendas and provide consumers with evidence-based information needed to make informed food choices. This study fills this research gap by evaluating the environmental sustainability and diet cost associated with adherence to the Mediterranean diet pattern in the US.MethodsDietary data from the National Health and Nutrition Examination Survey (2011–2018, n = 17,079) were merged with data on environmental impacts (greenhouse gas emissions, cumulative energy demand, water scarcity footprint), agricultural resource demand (land, fertilizer nutrients, and pesticides), and food prices from multiple publicly available databases. The Mediterranean Diet Score was used to evaluate adherence to the Mediterranean diet pattern. Multivariable linear regression models were used to evaluate the association between adherence to the Mediterranean diet pattern and environmental impacts, agricultural resource demand, and diet cost. Sensitivity analyses were used to evaluate adjustment of loss and waste and food-away-from-home prices.ResultsGreater adherence to the Mediterranean diet pattern was associated with lower greenhouse gas emissions (p < 0.001), land use (p < 0.001), fertilizer nutrient use (p < 0.001), and pesticide use (p < 0.001), higher water scarcity footprint (p < 0.001) and diet cost (p < 0.001), and no change in cumulative energy demand (p = 0.147). These changes were driven primarily by reduced intake of animal-sourced foods such as beef dishes, meat sandwiches, and dairy, as well as decreased intake of refined carbohydrate foods such as refined grain dishes and soft drinks.ConclusionsThis nationally representative study demonstrates that greater adherence to the Mediterranean diet pattern is associated with sustainability trade-offs. These findings have implications for the development of sustainable dietary guidelines and clinical practice guidelines that can be used to inform consumer food choices.

How Will Concrete Piles for Offshore Wind Power Be Damaged Under Seawater Erosion? Insights from a Chemical-Damage Coupling Meshless Method.

Based on the background of the continuously rising global demand for clean energy, offshore wind power, as an important form of renewable energy utilization, is booming. However, the pile foundations of offshore wind turbines are subject to long-term erosion in the harsh marine environment, and the problem of corrosion damage is prominent, which seriously threatens the safe and stable operation of the wind power system. In view of this, a meshless numerical simulation method based on smoothed particle hydrodynamics (SPH) and a method for generating the concrete meso-structures are developed. Concrete pile foundation models with different aggregate contents, particle sizes, and ion concentration diffusion coefficients are established to simulate the corrosion damage processes under various conditions. The rationality of the numerical algorithm is verified by a typical example. The results show that the increase in the aggregate percentage gradually reduces the diffusion rate of chemical ions, and the early damage development also slows down. However, as time goes, the damage will still accumulate continuously; when the aggregate particle size increases, the ion diffusion becomes more difficult, the damage initiation is delayed, and the early damage is concentrated around the large aggregates. The increase in the ion diffusion coefficient significantly accelerates the ion diffusion process, promotes the earlier and faster development of damage, and significantly deepens the damage degree. The research results contribute to a deeper understanding of the corrosion damage mechanisms of pile foundations and providing important theoretical support for optimizing the durability design of pile foundations. It is of great significance for ensuring the safe operation of offshore wind power facilities, prolonging the service life, reducing maintenance costs, and promoting the sustainable development of offshore wind power.

Modernization of Existing Level Crossings in the Areas of Construction of the European Standard Railway Track in Ukraine

Purpose. The article aims to analyze modern technological solutions to improve the level of safety at railway crossings, prevent emergencies and ensure uninterrupted traffic. Methodology. The study is based on the use of mathematical modeling and numerical methods to analyze the impact of modernization and new projects on the level of safety at railway crossings. Using computer modeling, mathematical models were built that reflect different scenarios of crossings. Based on the data obtained, the effectiveness of the proposed measures was assessed and recommendations for further optimization of the security system were developed. To evaluate the effectiveness of the proposed measures, a system of indicators has been developed to quantify the level of safety of railway crossings before and after modernization. These indicators take into account the daily intensity of train traffic through the crossing, the daily intensity of traffic on the road, the equipment of the crossing, the radius of the curve in the plan and the longitudinal slope of the road on the approaches to the crossing. Findings. An improvement of the method of the final accident rate, which is one of the most common tools for assessing traffic safety at railroad crossings, is proposed. The application of this method made it possible to quantify the level of risk of accidents and analyze the effectiveness of measures aimed at improving safety. The method is relatively easy to use and does not require complex mathematical calculations, and allows taking into account a wide range of factors affecting traffic safety at a level crossing, such as traffic volume, design features of the level crossing, availability of signaling, etc. The final coefficient was used to compare the safety level of different crossings and track the dynamics of changes in their equipment over time. This made it possible to identify crossings that require priority modernization, as well as to predict changes in the initial and calculated data and plan measures to introduce innovative technologies that improve traffic safety at crossings. Originality. An integrated model for assessing the safety of railroad crossings has been developed that takes into account not only traditional indicators (frequency of accidents, traffic intensity) but also new factors such as equipment reliability, human factor, and the impact of external conditions. The proposed model makes it possible to determine a more accurate risk forecast and choose optimal solutions for the modernization of level crossings, taking into account specific operating conditions. Practical value. The formulated recommendations can be used to develop effective programs for the modernization of railway crossings in Ukraine. The implementation of the proposed technologies will increase the level of traffic safety by 20% and reduce the average waiting time at a crossing by 15%. In addition, it will help to increase railroad capacity and reduce infrastructure maintenance costs.

Investment Feasibility Analysis of Rooftop Solar Power Plant for Spare Parts Supply Company

- A sustainable economic approach involves economic growth while preserving the environment. One solution that includes a green economy program that has received attention is the Solar Power Plant (PLTS), which utilizes sunlight as the main energy source to generate electricity. Increasing energy demand and concern for the environment, a spare parts supply company in Karawang, West Java, decided to do hybrid energy in the form of investment in renewable energy sources in the form of Solar Power Plant (PLTS) with an on-grid system installed on the roof of the factory. This study aims to evaluate the financial feasibility of the development of green economic efforts. The analysis includes various aspects, such as initial investment costs, operational costs, maintenance costs of the main plant materials (solar modules and inverters), potential savings in electricity costs, and projected return on investment within a certain period. The financial analysis methods used in this study include Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (DPP), and Profitability Index (PI) to assess the economic feasibility of the project. The results show that the DPP value is 2.43 years (less than 25 years), NPV is Rp 1,433,144,387.92 (greater than 1), IRR is 46.31% (greater than 5.75%), and PI is 1.95 (greater than 1). Based on these results, it can be concluded that the construction of a rooftop solar power plant with an on-grid system is a financially viable investment for a spare parts supply company in Karawang, West Java.

Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm

With the continuous growth of Industry 4.0 (I4.0), the industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for the sustainable development of organizations. Machinery is a critical component and key to the success of production in a smart industrial factory. Minimizing unplanned downtime (UPDT) poses a significant challenge in designing an effective maintenance system. In the era of Industry 4.0, the most widely adopted maintenance frameworks are intelligent maintenance systems (IMSs), which integrate predictive maintenance with computerized systems. IMSs are intelligent tools designed to efficiently plan maintenance cycles for each machine component in a smart factory. This research presents the application of a search algorithm named state space search (SSS) in conjunction with a newly designed IMS, aimed at optimizing maintenance routines by identifying the optimal timing for maintenance cycles. The design began with the development of a new IMS concept that incorporates three key elements: the automation pyramid standard, Industrial Internet of Things (IIoT) sensors, and a computerized maintenance management system (CMMS). The CMMS collects machine data from the maintenance database, while real-time parameters are gathered via IIoT sensors from the supervisory control and data acquisition (SCADA) system. The new IMS concept provides a summary of the total maintenance cost and the remaining lifetime of the equipment. By integrating with SSS algorithms, the IMS presents optimized maintenance cycle solutions to the maintenance manager, focusing on minimizing costs while maximizing the remaining lifetime of the equipment. Moreover, the SSS algorithms take into account the risks associated with maintenance routines, following factory standards such as failure mode and effects analysis (FMEA). This approach is well suited to smart factories and helps to reduce UPDT.

Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps

This paper introduces a novel thermoelectric generator (TEG)-powered Industrial Internet of Things (IIoT) device that addresses key limitations in the detection of steam leaks in industrial steam pipelines, particularly in steam traps. Existing solutions often rely on battery-powered or wired sensors, which are limited by high maintenance costs, short lifespans, or significant infrastructure investments. The proposed device operates without batteries, using waste heat to provide continuous power, and leverages LoRaWAN for long-range wireless communication, minimizing reliance on costly internal infrastructure. Additionally, the device integrates temperature differential (ΔT) and ultrasonic sensors with edge computing capabilities to enhance real-time leak detection and reduce dependency on cloud computing. By enabling precise, low-maintenance monitoring of steam systems in energy-intensive industries (e.g., petrochemical, pharmaceutical), this technology can significantly reduce energy losses, operational costs, and greenhouse gas emissions. Initial testing demonstrates the device’s ability to detect leaks accurately under varying industrial conditions, offering a robust, scalable solution for Industry 4.0 applications.