Maintenance Expenses Research Articles

Optimizing a Sustainable Power System with Green Hydrogen Energy Storage for Telecommunication Station Loads

Telecommunication stations situated in rural areas often rely on diesel generators as their primary energy source to meet electricity demand, given the absence of a power grid. However, this heavy dependence on diesel generators leads to escalated operational and maintenance expenses, while exacerbating global warming through greenhouse gas emissions. This paper proposes a shift towards a 100% hybrid renewable energy system integrated with hydrogen energy storage as a sustainable alternative. The proposed system incorporates photovoltaic (PV) panels, wind turbines, an electrolyzer, a fuel cell, a hydrogen tank, and a converter. Using HOMER Pro software, the optimal sizing of the system was determined, resulting in a configuration with 12.3 kW PV capacity, two 10 kW wind turbines, a 10kW fuel cell, a 20 kW electrolyzer, a 5 kg hydrogen tank, and a 17.3 kW converter. This configuration achieved a net present cost (NPC) of $155,705 and a cost of energy (COE) of $0.388/kWh, offering substantial cost-effectiveness. Compared to the base case relying solely on diesel generators, the system could avoid approximately 31,081 kg of CO₂ emissions annually. Finally, a sensitivity analysis was conducted to assess the impact of meteorological variations on the system’s economic outputs. The findings of this comprehensive study demonstrate the proposed hybrid system's feasibility in terms of both environmental sustainability and economic viability, presenting a sustainable alternative for off-grid telecommunication stations.

Synergizing Socioeconomic, Technological, and Environmental Factors Influencing the Adoption of High-Efficiency Irrigation Systems (HEIS): Insights from Highland Balochistan, Pakistan

Water plays a crucial role in sustaining human life and facilitating global economic development. Balochistan, the largest province of Pakistan, faces severe water scarcity primarily because of the unregulated extraction of groundwater aquifers. The scarcity of water resources can be effectively addressed by increasing Water Use Efficiency (WUE) achievable through the adoption of High-Efficienscy Irrigation Systems (HEIS). Despite substantial efforts by both public and private sectors, the adoption rate of efficient techniques remains notably low. Evaluating the factors influencing the adoption of high-efficiency irrigation methods and their resultant impact on water use efficiency is the need of time. In this study, an effort was made to study HEIS and its characteristics including the factors affecting the adoption decision, operation duration, and the economics of HEIS. The present study focused, on three main river basins using a multistage sampling technique in highland Balochistan. Three key basins namely Nari River Basin (NRB), Zhob River Basin (ZRB), and Pishin Lora Basin (PLB) Out of 18 basins in Balochistan were selected purposively. These basins are currently dealing with the issue of depleted groundwater table at a rate of more than 5-15 feet annually in the majority of their aquifers. A representative sample size of 300 farmers was chosen using a proportionate stratified sampling technique. Logistic regression was employed to analyze the data and the study findings revealed that the availability of off-farm income and higher education level were found to have highly significant and positive relationships with the likelihood of adoption. Farmers' age and the level of water scarcity were additional significant factors. Along with them, installation costs, maintenance expenses, and technological complexity were key barriers to HEIS adoption. To increase farmers' willingness to accept technology in highland Balochistan, organizations should undertake extension and educational training to increase knowledge and awareness of HEIS.

Cost Estimation for the Operation and Maintenance of Automated Monitoring and Early-Warning Equipment for Geological Hazards

Geological hazards impede regional economy sustainability. To limit their destructive impacts on human life and property, the Chinese government has independently developed automated monitoring and early-warning equipment, which has been deployed in over 250,000 locations nationwide, yielding effective early warnings. The smooth operation of this equipment necessitates substantial human, material, and financial resources for its maintenance. To allocate funds rationally, the Ministry of Finance of China has mandated the urgent establishment of budget standards for the operation and maintenance of automated monitoring and early-warning systems for geological hazards. Addressing the research gap in this area, this study meticulously develops a cost model, subcategorizing operating costs, maintenance costs, and management costs. Addressing the intricate issue of maintenance expenditures, this study ingeniously breaks down routine operations and urgent repairs stipulated in technical standards into personnel, materials, and vehicular needs for each equipment type. Considering the total manpower involved in equipment maintenance, the per-unit maintenance cost is determined. This method allocates costs to individual pieces of equipment, thereby sidestepping the quantification hurdle created by varying types and quantities of monitoring equipment at each monitoring site due to various geological disaster types and magnitudes, and technical personnel’s maintenance responsibility for multiple equipment types in a single operation. Finally, incorporating regional adjustment coefficients, we have formulated theoretical costs for the operation and maintenance of automated monitoring and early-warning equipment for geological hazards. By contrasting theoretical costs with actual project budgets, the error margin is within 2%. Following nationwide consultation, these theoretical costs have been officially endorsed as the budget standard. These standards will lay the groundwork for project budgeting and review, facilitate efficient fund utilization, and ensure the financial sustainability of monitoring and warning systems for geological hazards. Concurrently, this paper bridges the global lack in budget norms for the operation and upkeep of automated geological disaster monitoring systems. The cost calculation model introduced serves as a pivotal reference globally for the evaluation of analogous system’s operations and maintenance expenses.

Comparison of Ni-based SiC and B4C reinforcements on a TIG-coated AISI 1040 steel

Abstract In order to coat the surface of AISI 1040 steel using the TIG coating process, SiC and B4C are added to NiCrBAl powder at various rates. The microstructure and wear characteristics are then analyzed. Thus, it is intended to increase the AISI 1040 steel’s resistance to elements like abrasion and friction while lowering maintenance expenses. The ratio of B4C to SiC reinforcing powder affected the produced coating layer’s microstructure analyses and wear tests were carried out. Therefore, it has been found that the hard carbide phases and boron carbides generated in the coating layer have a favorable impact on wear, lowering weight losses and friction coefficients.

Real-time fatigue assessment of Floating Offshore Wind Turbine Mooring employing sequence-to-sequence-based deep learning on indirect fatigue response

Mooring lines in Floating Offshore Wind Turbines (FOWT) are subjected to significant environmental loading, leading to fatigue degradation over prolonged usage which necessitates real-time monitoring to ensure operational safety. Traditional fatigue assessment measures stress fluctuations under operational loads with expensive underwater sensors, which are costly and complex to install, especially for monitoring mooring at higher depths. This eventually increases the levelized cost of energy and maintenance expenses. This study proposes a cost-efficient fatigue monitoring method for catenary mooring using a novel sequence-to-sequence deep-learning (DL) approach that leverages platform motions as indirect fatigue response to infer fatigue assessment. By correlating FOWT platform motions with their impact on the fatigue life of mooring lines, the proposed method offers an alternative to costly underwater sensor measurements. In the absence of real data, the DL model is trained using simulated datasets for an OC4 semi-submersible wind turbine model in the OpenFAST open-source simulation package, capturing a wide range of tension fluctuations under diverse metocean conditions. Accordingly, this synthetic data has been treated as “real data” throughout this study. The trained network effectively captures the nonlinear relationship between platform motion and mooring fatigue response in real-time, enabling efficient assessment of fatigue-induced damage on mooring lines.

Health monitoring and fault analysis of induction motors: a review

Abstract Induction motors are in high demand for industrial applications due to their reliable and efficient performance compared to other electrical motors. Therefore, the implementation of a predictive maintenance scheme is necessary to minimise financial losses in terms of revenue and maintenance costs. This scheme involves health monitoring system, aimed to enhance motor reliability, extend their useful lifespan and reduce maintenance expenses. Furthermore, traditional health monitoring systems often involve human intervention, which can slow down the diagnosis process. Therefore, a self-sufficient automated system has evolved to improve motor performance and reliability. This paper presents a comprehensive study of induction motor failures and the various methods used for their diagnosis. Additionally, it also discussed the importance of predictive maintenance and the pivotal role played by automated systems in ensuring the sustained efficiency of induction motors in industrial settings.

Advanced Artificial Intelligence Drivers and IoT Network Systems for Data Collection and Network Transmission in the Industry 5.0 Era

The primary bases of Industry 5.0, the Internet of Things (IoT), artificial intelligence (AI), and big data (BD) may serve as a basis for applying the concepts of smart manufacturing, innovative goods and services, and predictive maintenance (PDM) into practice. Assessing the remaining useful life of components using failure data and state monitoring information constitutes the AI techniques for predictive maintenance (PDM), which are critical in Industry 5.0. However, it is sometimes challenging to identify the annotation of component usage, creating an open problem for obtaining accurately labeled information and understanding it. This problem needs to be rectified if Industry 5.0 progress is proceeded with. In order to solve the PDM job, this study presents a new data-driven decision system (DDS) based on AI that overcomes the earlier issue. It does by concentrating on an actual industrial use scenario that involves modern processing and monitoring machines. Specifically, the most fundamental components of the recommended DDS include cloud-based storage, data interpretation, predictive modeling, recognition of features using Principal Component Analysis (PCA), and data collecting. An efficient prediction model called the unique moth flame search-tuned light gradient boosting machine (MFS-LGBM) technique is presented in this work. By enabling the ongoing gathering of identified samples and offering a data analysis environment to assist the maintainer/operator, the suggested approach resolves the PDM task. The research findings showed that when compared to the previous approaches, the suggested approach accomplishes the greatest conceivable balance between comprehension, computational effort, and forecasting accuracy in terms of quality estimation. Based on the suggested methodology, the performance is examined in terms of RMSE (600.10), MAE (390.60), [Formula: see text] (0.790) and MAPE (25.70) measures. By incorporating it into the cloud structure, the suggested technique minimizes maintenance expenses and enhances production efficiency by improving machining processes, assisting maintainers, and delivering current operating risk alerts.

A Study on 128 Slice MDCT unit in a Tertiary Care Teaching Hospital with Special Emphasis on Work Flow and Break-Even Analysis

Purpose: To study the Cost Analysis of 128 Slice MDCT unit in a tertiary care teaching hospital Materials and methods: The study followed a biphasic design, comprising both retrospective and prospective components. The retrospective phase involved a record-based analysis conducted over a 3-month period, while the prospective phase included record review, observational methods, interviews, and material evaluation relevant to the study, carried out over the course of 1 month. Results: The cost of running the 128 Slice MDCT unit, including manpower, was calculated for a 3-month period, with an average of 28 cases per day. The unit handles a total workload of 54 TR (15 TR + 35 TR + 4 TR). Power is supplied by a 120KVA UPS located in the unit's UPS room. Depreciation for the building, with a lifespan of 100 years, is set at 1% of the capital cost. For an area of 51.2 sqm, the construction cost totals Rs 19,18,208/-, with a depreciation cost of Rs 47,955/- over 3 months. According to the CPWD manual, the annual maintenance cost is Rs 5104/- per sq.m., resulting in a maintenance expense of Rs 65,331/-. Additional costs for electricity, water, and linen over the 3-month period were also factored in. The breakeven point for the unit is estimated to be 744 days (approximately 2 years and 14 days). Conclusion: Getting each patient into and out of the procedure room in a timely manner can add minutes and maybe even hours to the daily schedules, allowing for the accommodation of a greater number of cases. Increasing the number of procedures performed per day or week can add to overall income.

Experiments for the Assessment of the Probabilistic Multistage Algorithm for Damage Detection in Flat and Curved CFRP Panels

The Probabilistic Multistage (PM) algorithm was developed by authors for identifying and localizing damages in isotropic plates. More specifically, PM algorithm consists of a fully automated probabilistic damage imaging methodology based on ultrasonic guided wave propagation and a 5-sensor array working in a pitch-catch approach. The algorithm processes the gathered data in three steps to identify key features associated with damage. The aim of this work is to assess the effectiveness of the PM algorithm on more complex structures. Specifically, the study investigates three cases of study, made of Carbon Fiber Reinforced Plastic (CFRP) composite, characterized by different geometries and layups. The algorithm is initially tested on panels with artificial damage in the form of a Teflon disk located in a specific location between the middle laminae of the panels, which is used to replicate the effect of delamination. In order to expand the experimental dataset without incurring additional costs or waste, new damage conditions are simulated by adding masses on the upper surface of the panels. Each plate is investigated considering three different damage sizes and 16 different damage locations. The proposed algorithm successfully detects damages both within and outside the sensor network. The PM algorithm produces a clear damage positioning map and a positioning (probabilistic field) range for the identified damage. This information can be used to assist operators in conducting inspections more efficiently by focusing on the highlighted areas, which may potentially lead to reduced maintenance and repair expenses.

ANALYSIS OF THE ESTABLISHMENT AND MAINTENANCE COSTS OF TABLE GRAPE PLANTATIONS ACCORDING TO THE PERGOLA SUPPORT SYSTEM

Evaluating the costs of establishment and maintenance of table grape plantations, grown through the Pergola management system, is a significant opportunity for winegrowers in the Republic of Moldova. This system, relatively new in the country, remains unadopted for many of the table grape producers. The present research highlights the methodological and technical aspects related to the costs associated with the cultivation of table grapes through the Pergola management system, which must be adapted to the specific climatic conditions of Moldova and to the current market requirements. The main objective of the research is to analyze the structure of costs that influence the yield of plantations in the long term, including the initial establishment expenses and the subsequent maintenance expenses. The author points out that although the maintenance costs of Pergola table grape plantations are high, the growing demand for quality grapes adds significant economic value to this vineyard management system. The research details the composition of the establishment and maintenance costs of table grape plantations, namely fertilization costs, irrigation costs, mechanized service costs. Concrete examples show what the structure of these costs is, thus demonstrating the importance of a correct assessment of costs in the process of establishing and maintaining table grape plantations.

Holistic opportunistic maintenance scheduling and routing for offshore wind farms

Despite the high growth of the offshore wind market, the economic benefits of wind energy sources are still being undermined by its high operation and maintenance expenses. On the one hand, high maintenance expenses are a direct result of offshore-specific challenges such as complex ocean meteorology, varying vessel accessibility, and shifting transportation requirements. Besides, component degradation and weather conditions largely limit the ability of turbine operators to estimate a workable maintenance time window. This study aims to develop a holistic opportunistic maintenance strategy to address the practical challenges of offshore wind farms. The proposed strategy starts by deriving the preventive maintenance interval of each turbine component based on its degradation trend and predictable cost rates. Then, each turbine is maintained in groups based on the maintenance opportunities arising from preventive maintenance, unexpected failures, and cable damage. Following that, given weather condition forecasts of key parameters (wind speed and wave height), the proposed strategy optimizes the daily allocations and vessel routes to maintain turbines in a timely and cost-effective manner. Finally, experimental results based on real-world data from an actual offshore wind farm demonstrate that the proposed strategy outperforms several universal maintenance strategies in key metrics. Compared to the simple, widely employed, and easy-to-implement strategies, it can help reduce the total cost by 85.9 %, 65.9 %, and 41.6 %, respectively. This work helps turbine operators implement comprehensive and cost-effective maintenance schemes, which can lead to tariff duction and wind farm promotion benefits in the long term.

Opportunistic maintenance for a novel load-sharing system with dependent degradation rate and volatility

For traditional degrading load-sharing systems, a typical assumption lies in the only effect of unit load on its degradation rate. However, motivated by a laser system example with positive correlation between degradation rate and volatility, we present a novel load-sharing system with general degradation rate-volatility-load correlation. Furthermore, to take account for unit economic dependence caused by sharable maintenance set-up cost, and the window period generated by the maintenance on some unit, opportunistic maintenance (OM) strategy is introduced to offer the opportunity of simultaneously maintaining another working unit under control-limit criteria. Based on the semi-regenerative process, the long-run average cost rate is minimized to obtain the optimal inspection interval, preventive maintenance and OM thresholds. Compared to the traditional condition-based maintenance strategy separately for each unit, the effectiveness of OM strategy is validated through numerical examples and case studies, offering broad managerial insights for engineers to reduce maintenance expenses.

Fund Allocation and Budget Utilization Rate in Relation to SUC Leveling Performance

In the academe, the realization of desired educational goals and objectives counts largely on the effective planning and management of school funds by the school administrators. The amount of funds allocated to schools by the government and the budget utilization rate of the recipient are some factors to be considered in meeting the programs, projects, and activities. This study aimed to determine the fund allocation and Budget Utilization Rate (BUR) in relation to SUC leveling performance among technological State Universities and Colleges (SUCs) of Region VIII in terms of Personal Services (PS), Maintenance and Other Operating Expenses (MOOE), and Capital Outlay (CO). The respondents were budget and planning officers in each SUC. Descriptive-evaluative research was utilized in this study since it assessed or examined the level of fund allocation and budget utilization rate in relation to SUC leveling performance. Percentage and mean were used to analyze the pertinent data, and coefficient correlation was applied to establish the significant relationships among the variables. Findings revealed that the bigger fund allocation with a correspondingly high budget utilization rate gave favorable SUC leveling performance. It is suggested to incorporate a conceptual model that links Information and Communications Technology (ICT) that accounts for budget allocation and utilization in future studies.

Enhancing road safety with the infrastructure-adaptable NDBA 2.0 concrete median barrier: An Italian experience

Road safety is a crucial global concern because of the high number of fatalities and injuries resulting from road crashes each year. Median crossover collisions are among the most dangerous crashes that happen on highways, frequently leading to serious or fatal injuries. The main approach to decreasing the occurrence of these types of crashes is the installation of median barriers. When the need for such installations arises, road agencies must choose from various options, including concrete barriers, cable barriers, or metal-beam guardrails. This paper is dedicated to the New Dynamic Barrier for Highways (NDBA 2.0), an innovative technology for median barriers developed by the Italian National Road Agency (ANAS), emphasizing its pivotal role in enhancing road safety. It incorporates high-tensile steel and advanced composites, offering robust protection while maintaining a lightweight profile. What distinguishes the NDBA 2.0 is its dynamic nature, featuring an intelligent system that seamlessly adapts to the road infrastructure. Its modular construction, with sections of only 200 cm, allows for easy installation and ensures compatibility across successive road segments. This adaptability reduces construction time while maintaining the highest standards of performance. From a road safety perspective, the NDBA 2.0 offers substantial advantages. Its design contributes to minimizing crash-related costs by reducing the severity of crashes, particularly in the transition zones. The barrier's design allows it to adapt to varying road conditions and traffic volumes, effectively addressing common installation challenges on existing roadways as well. Its ability to be directly supported on the road surface wear layer eliminates the need for costly foundation structures, facilitating quick installation and reducing maintenance expenses. The NDBA 2.0 barrier was designed to eliminate the need for future simulations in the design and verification of transitions between different barriers. For this reason, the NDBA 2.0 barrier has been tested in real-world conditions in class H4 and, consequently, is equipped with CE marking. This study offers a comprehensive analysis of the NDBA 2.0 barrier, whose implementation may provide significant benefits for road safety. Continued research, collaboration, and widespread adoption of the NDBA 2.0 barrier can further enhance road safety on a global scale.

Advances and challenges in friction pendulum bearings for seismic isolation systems

Friction pendulum bearings (FPBs) have emerged as critical structural isolation devices in the field of earthquake engineering. Extensive research and development efforts have led to the exploration and refinement of these bearings, resulting in the creation of various models with unique structural configurations and superior mechanical properties. This paper offers an in-depth review of the friction materials employed in FPBs, alongside an analysis of the mechanical performance and advancements in research regarding single-pendulum, double-pendulum, multiple-pendulum, and other types of friction bearings. Characterized by their low sensitivity and high stability, these bearings are adept at resisting seismic forces, thus providing dependable isolation protection for structures. This review also includes a succinct examination of the seismic performance and engineering applications of these isolation structures. With robust self-centering capabilities, excellent isolation, and energy dissipation mechanisms, FPBs are capable of quickly resuming normal operations post-seismic events. This reduces structural damage and maintenance expenses, significantly improving the seismic resilience of structures. Moreover, the paper outlines the current challenges in the research and development of FPBs and suggests future research directions, including optimizing friction materials, enhancing the design and performance of isolation structures, and improving the seismic performance and engineering application efficiency of FPBs. By identifying underexplored areas and synthesizing findings differently, this review provides a comprehensive and novel perspective that advances the field of earthquake engineering.

Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.

Exploring key metrics of sterilization services using cost analysis

ABSTRACT Sterilization services not only impact service delivery processes directly but also account for a substantial portion of total costs. Therefore, accurate resource measurement becomes imperative for efficient resource utilization. This research aims to draw a framework for calculating the package and autoclave unit cost within a sterilization unit and to pinpoint sterilization unit-specific findings distinct from those of other service provider departments. To achieve this, we collected and analyzed cost data from a public hospital in Trabzon, Türkiye, for 2021 and 2022. Subsequently, the unit cost of the package and autoclave of the sterilization unit was calculated based on the collected data. Our analysis considered various cost components, such as equipment maintenance, personnel salaries, and utility expenses. Through our research, we identified key factors influencing the sterilization process. The findings guided decision-makers in prioritizing areas for efficient and sustainable service provision. Furthermore, we emphasize the importance of our developed framework in cost analysis, highlighting its potential impact on resource management practices in healthcare settings. Additionally, insights into decision-making processes regarding in-house versus outsourced provision of sterilization services can inform managerial decisions and resource allocation strategies more effectively.

Monocular Visual Pig Weight Estimation Method Based on the EfficientVit-C Model

The meat industry is closely related to people’s daily lives and health, and with the growing global population and increasing demand for meat, the development of efficient pig farming technology is particularly important. However, China’s pig industry still faces multiple challenges, such as high labor costs, high biosecurity risks, and low production efficiency. Therefore, there is an urgent need to develop a fast, accurate, and non-invasive method to estimate pig body data to increase production efficiency, enhance biosecurity measures, and improve pig health. This study proposes EfficientVit-C model for image segmentation and cascade several models to estimate the weight of pigs. The EfficientVit-C network uses a cascading group attention module and improves computational efficiency through parameter redistribution and structured pruning. This method uses only one camera for weight estimation, reducing equipment costs and maintenance expenses. The results show that the improved EfficientVit-C model can segment pigs accurately and efficiently the mAP50 curve convergence is 98.2%, the recall is 92.6%, and the precision is 96.5%. The accuracy of pig weight estimation is 100 kg +/− 3.11 kg. On the Jetson Orin NX platform, the average time to complete image segmentation for each 640*480 resolution image was 4.1 ms, and the average time required to complete pig weight estimation was 31 ms. The results show that this method can quickly and accurately estimate the weight of pigs and provide guidance for the subsequent weight evaluation procedures of pigs.

Optimal preventive maintenance: Balancing reliability and costs in the electricity market

This study addresses the challenge of achieving optimal preventive maintenance within power systems, aiming to balance reliability and costs effectively. The primary focus is on understanding the relationship between preventive maintenance expenditures and the failure rate of essential transmission components, particularly transformers in substations. We contribute to the existing literature by providing two key insights. Firstly, we establish a theoretical framework for determining the optimal level of preventive maintenance, which can be extended across various electricity facilities. By analyzing the relationship between preventive maintenance costs and failure rates, our goal is to identify the investment level that ensures a reliable power system while minimizing financial burdens. Secondly, we explore the functional form that accurately characterizes the link between preventive maintenance costs and failure rates. The diminishing marginal rate of failure, dependent on different functional forms, highlights the variability of optimal preventive maintenance expenses. Utilizing Lasso regression, we identify the functional form that optimally characterizes the relationship between outage occurrences and maintenance costs. These insights extend well beyond the confines of the Korean power industry, offering a sustainable approach to managing transmission facilities and enhancing the overall stability and efficiency of global electricity networks.

Fault Detection of Wheelset Bearings through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and Support Vector Machine

This study aims to detect faults in wheelset bearings by analyzing vibration-sound fusion data, proposing a novel method based on Grey Wolf Optimizer (GWO) and Support Vector Machine (SVM). Wheelset bearings play a vital role in transportation. However, malfunctions in the bearing might result in extensive periods of inactivity and maintenance, disrupting supply chains, increasing operational costs, and causing delays that affect both businesses and consumers. Fast fault identification is crucial for minimizing maintenance expenses. In this paper, we proposed a new integration of GWO for optimizing SVM hyperparameters, specifically tailored for handling sound-vibration signals in fault detection. We have developed a new fault detection method that efficiently processes fusion data and performs rapid analysis and prediction within 0.0027 milliseconds per data segment, achieving a test accuracy of 98.3%. Compared to the SVM and neural network models built in MATLAB, the proposed method demonstrates superior detection performance. Overall, the GWO-SVM-based method proposed in this study shows significant advantages in fault detection of wheelset bearing vibrations, providing an efficient and reliable solution that is expected to reduce maintenance costs and improve the operational efficiency and reliability of equipment.