Maintenance Costs Research Articles

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries

BackgroundIn low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data.MethodsWe implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software.ResultsThe model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%.ConclusionsWe demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Precision forecasting of grinding wheel Wear: A TransBiGRU model for advanced industrial predictive maintenance

In intelligent manufacturing, accurate prediction of grinding wheel wear is essential to reduce maintenance costs and improve production efficiency. To achieve accurate forecasts, this paper introduces a grinding wheel wear prediction model, TransBiGRU, incorporating a Transformer encoder, Bidirectional Gated Recurrent Unit (BiGRU), positional encoding layer, and position-wise feedforward layer. The model extracts features by analyzing current signal characteristics in basic statistical, time, and frequency domains during the machining process. Training is conducted through K-fold cross-validation to ensure model stability. The experimental results indicate that the model achieved good performance with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) evaluation metrics, obtaining values of 2.0898, 3.262, and 0.9338, respectively. Systematically reducing modules validates the importance of each module in enhancing predictive capabilities. This research achieves the practical application of low-cost current sensors in optimizing maintenance plans and reducing downtime in predicting grinding wheel wear.

Natural low corrosive phytic acid electrolytes enable green, ultrafast, stable and high-voltage aqueous proton battery

Proton is a promising charge carrier for aqueous rechargeable batteries due to its small ionic radius, fast diffusion kinetics and great abundance in nature. However, commonly-used proton battery electrolytes are strong acids, such as sulfuric acid, phosphoric acid, etc., which always leads to the notorious rapid corrosion of electrode and metal components, and thus the poor cyclability, the limited materials option and high maintenance cost. Here, we reported a new-type high-concentration phytic acid (PA) electrolytes, which exhibits marvellous superiorities including environmental friendliness, low corrosiveness, high ionic conductivity (133.3 mS cm−1) and wide working potential window (∼2.9 V) in contrast to conventional acid electrolytes. The experimental and calculation results indicated that the scarcity of free water in high-concentration PA electrolytes and the desolvation effect of the preferentially absorbed PA interfacial layer on the electrode surface work in synergy to inhibit the water activity and thus prevent electrode dissolution and side reactions. Benefiting from this, the assembled K0.2VO0.6[Fe(CN)6]0.8•6H2O (VHCF)//MoO3 button-type proton battery shows an average discharge voltage of 0.7 V with a pair of well-defined redox peak at about 1.4 V, great rate performance, excellent cycling stability (87.9% capacity retention after 1000 cycles at 5 A g−1) and superb coulombic efficiency at various current densities (1 A g−1–50 A g−1). Attributed to good anti-freezing properties of the electrolytes, the proton battery can also run normally at -60 °C and exhibit competent performance (a capacity retention of 45 mAh g−1 at 0.1 A g−1 after 540 cycles). Such green low corrosive electrolyte will be fully compatible with conventional battery fabrication process, and open brand new avenue for the development of aqueous proton battery.

Fretting wear behaviour of high strength alloy steel induced by plasma nitriding and post-oxidation

Fretting wear is caused by a relative movement of the material surface on a small scale. Finding ways to reduce fretting wear is of great importance for extending equipment life and reducing maintenance costs. With the increase of load, the effecrt of fretting wear adhesion of surface treated material becomes more obvious. The materials treated with post-oxidation have stronger wear adhesion than those treated only with plasma nitriding, but the total wear volume is reduced. In this paper, the fretting wear of high strength steel alloy with different surface treatments is simulated by the finite element method. The coefficient of friction and wear volume can be obtained from the experimental results, and the wear coefficient is calculated using the wear energy model. The predicted two-dimensional wear profile curves (U-shape and W-shape wear profiles) under different wear conditions are compared with the experimental results. The W-shape wear profile is used to describe the adhesion state of the worn surface. In addition, the surface deformation and mechanical properties of U-shape and W-shape wear profiles are analysed. The simulation results show that double-sided wear and the addition of adhesive layer is important for the prediction of wear characteristics.

Cost-effective high-gain DC-DC converter for elevator drives using photovoltaic power and switched reluctance motors

Cost-effective high-gain DC-DC converter for elevator drives using photovoltaic power and switched reluctance motors

Smart Borewell Rescue System through Wireless Monitoring using Artificial Intelligence

Modern urbanization and expansion of cities has boomed since a decade, water shortage and inefficient supply of water from conventional water reservoir and supply system has led to increase in number of domestic bore well installation. And cost of installation and maintenance is easy and affordable hence more and more people are opting for their personal bore well, rather depending of City Corporation for water supply. This has drastically increased the accident in the site of bore well especially small kids are vulnerable and many incidents has occurred where small kids get struck in open bre well and some being sunk into the depth. The objective of the project is to construct and design a bore well rescue robot, which can lift and rescue the kids who fall into well.

Technological innovations and optimized work methods in subsea maintenance and production

Subsea maintenance and production represent critical aspects of offshore operations, vital for sustaining energy production and ensuring operational efficiency. However, these endeavors face numerous challenges, including the complexities of deepwater environments, harsh weather conditions, and the high costs associated with traditional maintenance methods. To address these challenges, this paper explores the integration of technological innovations and optimized work methods in subsea operations. Technological innovations play a pivotal role in revolutionizing subsea maintenance and production. Remote monitoring and control systems enable real-time data collection and decision-making, enhancing operational visibility and efficiency. Autonomous underwater vehicles (AUVs) offer the capability to conduct inspections and repairs in remote and hazardous environments, reducing human intervention and associated risks. Robotics and automation further streamline maintenance tasks, improving accuracy and reducing downtime. Advanced materials and coatings enhance equipment durability and corrosion resistance, prolonging asset lifecycles and reducing maintenance requirements. In parallel, optimized work methods offer strategic approaches to enhance subsea operations' effectiveness. Predictive maintenance strategies leverage data analytics and machine learning to anticipate equipment failures, enabling proactive interventions and minimizing downtime. Condition-based monitoring facilitates real-time assessment of asset health, enabling timely maintenance interventions and cost savings. Integrated asset management systems provide holistic insights into asset performance and facilitate informed decision-making. Lean operations and continuous improvement methodologies further optimize workflows, driving operational excellence and cost efficiency. Through case studies and industry examples, this paper highlights the successful implementation of technological innovations and optimized work methods in subsea maintenance and production. Furthermore, it explores future trends, regulatory considerations, and the importance of industry collaborations in shaping the future of subsea operations. Ultimately, the integration of these approaches offers a pathway towards enhanced operational efficiency, reduced costs, and sustainable subsea operations. Keywords: Technological Innovations, Optimized Work Methods, Subsea Maintenance and Production.

Transforming equipment management in oil and gas with AI-Driven predictive maintenance

The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.

Cucumber under Controlled Environment Polyhouse: A Step Forward to Enhance Farmers Income

Present study was conducted to see profitability of cucumber crop when grown under polyhouse. Controlled environment crops have greater consistency and production is more reliable than uncontrolled open field crops. Technology is used to manage and manipulate the growing conditions to reduce uncertainty and optimise crop growth. Particularly, cucumber crop was uneconomical when grown under open field conditions due to its climate sensitiveness. Present study was conducted in cucumber which was being cultivated in environment-controlled poly house. The cost return analysis showed that the total cost of cultivation per 1000 m2 area was Rs. 307746 with an output value of Rs.888960 resulted in Benefit Cost Ratio of 2.89. As the poly house involves initial establishment costs besides maintenance costs every year. This component was amortized to be included in Total Fixed Cost component (TFC) of cost analysis. Similarly, the bed prepared for cucumber sowing also amortized as it lasts for five years once prepared. As most of the farmers were facing initial capital issues for establishing polyhouse, majority of farming fraternity till unable tap the full benefits of cucumber crop. By utilizing the policies of government, cucumber cultivation under poly house is economically beneficial.

48 Use of 4,500 FTU/kg of phytase on performance and blood parameters of finishing pigs

Abstract Phytase hyper-dosing is a recent concept in nutrition based on the use of high doses of the enzyme with growth promoting effect. Supplementary release of inosotol, rapid and strong destruction of phytic acid and other non-phosphoric effects are proposed mechanisms for the positive results found on pig growth. The hypothesis of this concept would be to accelerate this destruction of the phytate improving feed conversion ratio (FCR). A second hypothesis is that this increased destruction of the phytate may generate decreased concentrations of cortisol, because the inositol has insulin-like effects, reducing glycemy and reducing cortisol concentrations and FCR. To evaluate these hypotheses, two different studies were conducted, in Brazil and Mexico, using Quantum Blue (AB Vista) in two formulation strategies, Control Group - a dose and nutritional matrix of 750 FTU/kg; Hyper-dosing Group - a dose strategy of 4,500 FTU/kg (but with the same matrix of 750 FTU/kg). In the Brazilian evaluation (Trial 1 - RESTRICTED feed consumption trial), 364 animals/treatment were used, divided into 26 replications of 14 animals each (13 pens of males and 13 pens of females). In the Mexican evaluation (Trial 2 – NO restricted feed consumption trial), 400 animals/treatment were used, divided into 8 replications of 50 animals each, all animals used in this evaluation were males. In both studies, body weight gain, feed intake and FCR were evaluated. In the Brazilian study, in addition, the blood cortisol concentration of the animals was also evaluated. The data from each experiment were analyzed using the GLM platform (Minitab 18), and ANOVA used to assess differences on treatments (P ≤ 0.05). As presented in Table 1, in Trial 1, no differences were noted (P > 0,05) with the Hyper-dosing strategy for males; however, for females, this strategy improved (P ≤ 0.05) the FCR in Hyper-dosing Group versus Control Group. This difference in results between sexes can be explained by the feeding rate adopted in this experiment, which was similar for all animals (highly restrictive), what may have penalized some animals more because of the greater maintenance costs. The analysis of the blood parameters of these animals demonstrated a reduction (P ≤ 0.05) in cortisol concentrations, indicating that the use of phytase Hyper-dosing can be even better in situations of thermal or environmental stress. Also presented in Table 1, in the Mexican study the results were possible to observe a statistical trend (P ≤ 0.05 and > 0.10) for the use of the Hyper-dosing concept for FCR. In conclusion, the use of 4,500 FTU/kg of Quantum Blue in the last 30 d of finishing pigs helps to improve performance and reduce blood cortisol concentrations of these animals, especially in situations where the production efficiency rate is moderate.

Draft-free air conditioning through split membrane ceiling system: An exploratory study

The draft caused by convective air-conditioning is a primary contributor to thermal discomfort, particularly in localized cooling scenarios. While radiant systems offer a windless alternative, their condensation risks and maintenance costs bring concerns. In response, our research proposes an air-based radiation-convection hybrid system that’s draft-free and easy to implement. By suspending a breathable, flexible, textile-based membrane beneath the ceiling, the system mitigates drafts from ceiling-mounted air conditioners and leverages the membrane’s vast surface area for radiation. This work is an exploratory study focused on a retrofit split membrane system laying below the commercially packaged air conditioner (PAC). Three independent experimental studies were conducted. The first is a full-scale experiment assessing the membrane’s impact on PAC system operation and heat load management. The second is fundamental research to determine the permeation and airflow deceleration properties of different membranes. The third is a practical assessment in a self-study room retrofitted with the split membrane, aiming to determine the possibility of maintaining draft-free conditions. Results indicate that membranes with high laying ratios risk airflow short-circuiting. It can be reduced by adjusting the laying ratios and altering the laying pattern of the membrane, which also improves perimeter heat load management. Moreover, the permeation characteristics of various membrane materials were examined through flow and velocity attenuation coefficients. Besides material considerations, preliminary observations suggest that the membrane’s curvature could influence its permeation. Furthermore, the temperature and velocity data preliminarily affirmed the anti-draft performance of the split membrane ceiling system.

Transforming surgical landscapes: obstacles in the integration of robotic surgery in Pakistan -is cost the only limiting factor?

The advantages of Robotic Assisted Surgery (RAS) over laparoscopic surgery encompass enhanced precision, improved ergonomics, shorter learning curves, versatility in complex procedures, and the potential for remote surgery. These benefits contribute to improved patient outcomes which have led to a paradigm shift in robotic surgery worldwide and it is now being hailed as the future of surgery. Robotic surgery was introduced in Pakistan in 2011, but widespread adoption has been limited. The future of RAS in Pakistan demands a strategic and comprehensive plan due to the substantial investment in installation and maintenance costs. Considering Pakistan's status as a low to middle-income country, a well-designed economic model compatible with the existing health system is imperative. The debate over high investments in robotic surgery amid unmet basic surgical needs underscores the complex dynamics of healthcare challenges in the country. In this review, we discuss the potential benefits of robotics over other surgical techniques, where robotic surgery stands in Pakistan and the possible hurdles and barriers limiting its use along with solutions to overcome this in the future.

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

Uniform Modeling of Heat Production Costs in Single-Family and Multi-Family Houses

To ensure a fair and extensive comparison of different heating systems four representative buildings including their heat demand for space heating and domestic hot water were defined. A model for the heat production costs has been developed based on existing methods like the VDI 2067 part 1 [1] and the simplified approach for the calculation of the "Levelised Cost of Heat" (LCoH) of the IEA SHC Task 54 "Price Cost reduction in solar thermal systems" [2]. The developed model includes the costs of purchase, installation, operation, maintenance and disposal and was implemented in an EXCEL calculation tool which enables the calculation of the total life cycle costs (LCC), the levelized cost of heat (LCoH) and the CO2 emissions over the lifetime of the system.

Factors influencing farmers’ adoption of solar water-pumping systems in Gujarat

Abstract Agriculture has played an important role in the growth of the Indian economy. Water and electricity are essential inputs for agriculture today. India produces the third-largest amount of electricity in the world, behind China and the USA. As an incentive to increase production, most states provide free electricity to farmers. Free electricity has decreased groundwater levels and increased electricity consumption. The objective of this study was to find out the factors that influenced farmers to adopt a solar water-pumping system in Gujarat. The samples were randomly selected. This study examined the entire state of Gujarat. One hundred and fifty farmers who adopted solar water-pumping systems were interviewed, including 50 banana farmers, 50 cotton farmers and 50 groundnut farmers. Primary data were collected through an interview. The adoption of solar water-pumping systems by farmers was identified using factor analysis. SPSS software was used to analyse the data collected. In this study, only two factors contributed to the variance of 59.469%. The adoption of solar water pumps by farmers was influenced by government policy and economic benefits. An economic benefit variance of 33.20% was found, while a government policy variance was 26.27%. A solar water-pumping system has low initial costs, flexible credit policies motivate adoption, solar water-pumping systems can save electricity, and maintenance and repair costs are affordable. Government policy that includes solar water-pumping systems is motivated by awareness, subsidies for adoption and a fair price to sell energy surpluses. Farmers are recommended to adopt a solar water-pumping system in conjunction with a drip irrigation system to receive additional income. The successful scheme will be recommended to other states in India for implementation.

Industrial-Based GSM Water Leakage Detection, Monitoring and Controlling System: A case of North Rift Valley Water Agency in Kenya

The current system for detecting and monitoring water leaks in Kenyan industries is manual and costly. Despite emerging new technological trends, many industries lack automated systems to detect, monitor, and control water leakage due to the high cost of maintenance and installation. This study's objective was to develop an automatic, remote, and real-time detection, monitoring, and, control system for water leaks. The system is made up of two nodes, one at the source and one at the destination or tap. The two nodes are made up of an ESP microcontroller, which is used to control all the connected components. The use of the ESP 32 microcontroller was efficient due to its ability to provide WI-FI. Aside from the solenoid valve, which was used to turn the water flow on or off in the event of leaks, the system also includes the FY-201 water flow sensor, which was used to gauge the amount of water flowing through the pipe. Water leakage is detected when the volume of water passing through the two sensors differs in terms of volume, indicating that a water leakage has just occurred. Thing-Board, an IoT-based platform used to monitor and visualize data from various devices connected, was used for real-time monitoring, visualization, and control. The developed system was tested with different water service providers, and the results showed that the system responds positively to water leakage parameters with capability of monitoring water leakages in a real time.

ANALYSIS OF THE PROPERTIES OF COATINGS USED TO PROTECT THE PUMP AND COMPRESSOR PIPE

In this article considers basic properties of protective coatings applied to the inner wall of tubing in order to extend pump and compressor pipe (PCP) service life PCP and minimize operational risks. The main complicating factors, such as scale deposits, corrosion aggressiveness, mechanical impurities, etc., arising during PCP operation are specified, groups of test media and test parameters for grading different types of coatings according to their resistance to complicating factors are singled out. Influence of various aggressive media on PCP, such as distilled water, petroleum product simulator, etc., coatings is described, and test media and test parameters for protective coatings are reviewed. The basic methods for determining the physical and mechanical characteristics, such as determining adhesion, thickness and dielectric continuity, of coatings are given, and brief informative and descriptive references for each of them are given. Recommendations are given for testing protective coatings on the inner surface of PCP for compliance with technical requirements, which ultimately allows selecting materials most resistant to aggressive field environments. The relevance of studying the influence of aggressive media on the protective coatings of the inner wall of tubing is due to the need to increase the level of reliability and durability of technical systems in operating conditions. The work is of interest to specialists in the field of materials science, technical expertise and tubing design, as well as researchers involved in the development of new materials and methods for surface protection in operating conditions. The results obtained will optimize the choice of protective coatings for specific operating conditions, which helps to increase the service life of tubing and reduce maintenance costs. This is of direct importance for reducing the risks of accidents and the environmental consequences of oil and gas production processes. In addition, the development of innovative materials and methods for protecting the surfaces of the inner wall of the tubing contributes to improving the efficiency of the operation of oil and gas wells, as well as helps to reduce equipment downtime.

Joint planning for PV-SESS-MESS in distribution network towards 100% self-consumption of PV via consensus-based ADMM

The integration of distributed photovoltaic (PV) has become a prominent characteristic of distribution network (DN). However, achieving 100% PV self-consumption poses challenges due to intermittent generation and varying load demands. To address these challenges, this paper proposes an innovative joint planning method for PV, stationary energy storage system (SESS), and mobile energy storage system (MESS) using the consensus-based alternating directional multiplier method (ADMM). First, to account for multiple scenarios with real-world variations, a two-stage clustering method with clustering by fast search and finding of density peaks and k-means is employed. Second, a joint planning model for PV, SESS, and MESS is developed to minimize the total cost of DN, including installation, operation, and maintenance costs. The model optimizes the PV capacity, SESS capacity, and MESS capacity, as well as their locations. Third, a distributed planning framework is proposed using consensus-based ADMM, which decomposes the problem into a PV planning subproblem under the worst-case scenario and energy storage system planning subproblems under multiple scenarios. Finally, the proposed method is demonstrated on an IEEE-33 node system, and the results show the method effectively enhances the access capacity of PV by 11.80% and leads to a reduction in the total cost.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts

Spare parts redundancy is an effective way to improve system reliability and prolong system lifetime. Generally, preventive maintenance and inventory management of a standby system can significantly reduce operating and maintenance costs, particularly crucial in industrial systems. Additionally, during storage, spare parts may experience degradation failure due to internal mechanisms and sudden failure due to external shocks, complicating the health management of the standby system. Unfortunately, few existing studies consider the impact of spare parts’ competing failures in standby systems. To address this gap, this paper proposes a novel method for predicting the lifetime and optimizing the maintenance policy of nonlinear degradation standby systems, considering both degradation and sudden failures of spare parts. Firstly, we established the degradation model under the competing failure of spare parts and derived the analytical formula for life prediction of the standby system in the presence of competing failures, assuming that sudden failure follows the Weibull distribution. Furthermore, we developed a joint optimization model for replacement maintenance and inventory management based on predictive information, aiming to minimize the expected cost, in which the block replacement interval and the number of spare parts are treated as decision variables. Finally, the effectiveness and potential application value of the proposed method are verified through numerical simulation and a case study.