Maintenance Strategies Research Articles

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.

Research Progress on Novel Inspection of Oil and Gas Pipeline Integrity

Oil and gas pipeline integrity is the key to ensure the safety of energy transportation, with the development of science and technology, the research and application of new detection technology has been increasingly emphasized. This paper firstly describes the background and practical significance of the selected topic, aiming at exploring more efficient pipeline inspection methods to ensure the stable operation of energy networks. In terms of theoretical framework, we have sorted out the theoretical system of integrity management, including the core elements of risk assessment, inspection technology, and maintenance strategy, which provides a theoretical basis for the subsequent research. Then, we systematically review the latest progress of oil and gas pipeline integrity testing at home and abroad. From the dimensions of physical principle and application practice, we analyze the advantages and disadvantages of conventional ultrasonic and magnetic particle flaw detection, as well as the emerging acoustic emission and fiber grating inspection technologies, and show the application cases of each technology in actual projects. Through comparative analysis, we find that although conventional technologies meet the inspection needs to a certain extent, new inspection technologies such as remote monitoring based on the Internet of Things (IoT) and artificial intelligence-assisted diagnosis are gradually becoming a research hotspot due to their high precision, high efficiency and intelligent features. The development of these technologies not only improves the comprehensiveness and accuracy of inspection, but also provides strong support for realizing the whole life cycle management of pipelines. In the summary review section, we emphasize the future development trend of oil and gas pipeline integrity inspection, including the integration and intelligence of technology, as well as the deep mining and application of data. At the same time, we also point out the challenges in the current research, such as the unification of inspection standards and the establishment of data sharing mechanisms, which require the joint efforts of the research community and the industry to achieve the continuous improvement of oil and gas pipeline safety. To summarize, this paper aims to comprehensively show the research progress of new detection technology of oil and gas pipeline integrity, provide reference for researchers and engineering practitioners in related fields, and promote the further improvement of China's oil and gas pipeline safety management level.

Visual Quality Analysis of Diyarbakır Sur District's Historical Buildings and Surroundings

Historical sites are structures, monuments, or areas that carry the traces of the past and hold significant importance in the collective memory of society. The cultural, historical, and architectural value of historic places is important for visual perception. Visual quality analysis aims to assess the physical condition and appearance of these historic places and to identify their conservation and maintenance needs. This analysis plays an important role in the decisions to be taken for the preservation of the place. The parameters that influence visual quality reflect the preservation and maintenance of the place, the visitor experience, the importance of cultural heritage for conservation, tourism and economy, and social awareness. Visual perception of historic places refers to how people perceive these places and how this perception is associated with the historical, cultural, and architectural features of the place. The visual perception of historic places may vary depending on various factors. The visual perception of historic places is important for their preservation, visitor experience and the continuation of cultural heritage. While a positive visual perception of historic places can make them more valuable and important in society, a negative perception can damage their reputation and make it difficult to preserve them. For this reason, the visual perception of historical places is an important factor that should be taken into account in determining conservation and maintenance strategies and in the promotion of places. In this study, thirty historical places in Diyarbakır Sur District were evaluated by 10 experts according to visual quality parameters. As a result, with the scores obtained in the visual perception of historical places, suggestions were made for the protection of these places, their importance in terms of tourism and the continuation of cultural heritage.

Enhanced fault detection in energy systems using individual contextual forgetting factors in recursive principal component analysis

Improving maintenance strategies is an important component in reducing wastes of energy in industrial systems and especially energy systems, as they represent a large proportion of our total energy use. The use of fault detection methods can help transition to proactive maintenance methods and automate the detection of faults using data from the systems themselves. However, the presence of normal operational changes must be accounted for to avoid false alarms, as static fault detection methods cannot handle them. A recursive fault detection method, specifically recursive principal component analysis (RPCA), is applied to real building data in this paper to aid in addressing this challenge. Additionally, improvements to the aspect of updating forgetting factors, which are integral to RPCA's functionality, is also proposed which allows for enhanced adaptability. The results show that the improvement increases adaptability when setpoints are changed and provides similar performance otherwise. Lastly, the application of this was shown to significantly reduce false alarms in the building application, while still detecting the known faults.

Resilience-Based Restoration Model for Optimizing Corrosion Repair Strategies in Tunnel Lining

In tunnel engineering, the corrosion of steel rebar is a critical factor leading to structural degradation and failure, causing a decline in load-bearing capacity, deformation, and cracking. For decision-makers, identifying the optimal timing for tunnel maintenance and selecting effective repair strategies is of paramount importance. This study introduces a resilience-based restoration model to analyze tunnel failure due to corrosion throughout its service life and to optimize the timing and selection of maintenance strategies. The model generates time-variant failure curves by constructing limit equilibrium equations. The entropy weight method is employed to quantify and weight the impact of various failure modes, determining the timing for maintenance when the failure curve exceeds a predefined threshold. Additionally, the model's uncertainty is effectively reduced through regular inspections and Bayesian updating methods, enhancing prediction accuracy. The study further incorporates a resilience index and a benefit index to provide a quantitative assessment of maintenance plans, assisting decision-makers in selecting the optimal strategy. By exemplifying the model with a case study of steel rebar corrosion in a tunnel, this paper demonstrates the model's applicability and offers a new scientific approach for quantitative maintenance decision-making in tunnel engineering.

Degradation Modeling and RUL Prediction of Hot Rolling Work Rolls Based on Improved Wiener Process.

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model's adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model's potential as a reliable tool for real-time RUL prediction in industrial applications.

Maintenance Therapy Post-Hematopoietic Stem Cell Transplantation in Acute Myeloid Leukemia.

High-risk acute myeloid leukemia has been associated with a poor outcome. Hematopoietic stem cell transplantation (HSCT) represents the only curative option for eligible patients. Relapse after HSCT is a dramatic event with poor chances of survival. With the aim of reducing the rate of post-HSCT relapse, maintenance treatment has been investigated in this setting. Results from clinical trials suggest an advantage in the use of a maintenance strategy; however, standardized guidelines are not yet available due to the lack of prospective clinical trials. In this review, we have reported the most important strategies adopted as post-HSCT maintenance, highlighting their efficacy, but the current research also opens questions.

Research on wind turbines preventive maintenance strategies based on reliability and cost-effectiveness ratio

Purpose The purpose of this study is to optimize the operational efficiency of the entire system by developing a reasonable maintenance strategy for wind turbines that improves component reliability and safety while reducing maintenance costs. Design/methodology/approach A hybrid incomplete preventive maintenance (PM) model based on boundary intensity process is established to give dynamic PM intervals for wind turbines using an iterative method with reliability as a constraint; the selection method of PM and replacement is given based on the cost-effectiveness ratio, which in turn determines the optimal number of PM for wind turbines. Findings The reliability is used to obtain the components’ maintenance cycle, and the cost-effectiveness ratio is used to select the number of maintenance times, thus, getting the optimal maintenance strategy. The validity of this paper’s method is verified by arithmetic cases, which provides a new method for formulating a reasonable PM strategy for wind turbines. Practical implications The wind turbine preventive maintenance strategy for Boundary intensity process proposed in this paper can scientifically formulate the maintenance strategy, optimize the cost-effectiveness per unit of time of the wind power generation system, and solve the problems of difficulty in formulating a reasonable maintenance strategy for the wind turbine components and high operation and maintenance costs. Originality/value In this paper, the authors describe the failure pattern by a Boundary intensity process, establish a hybrid incomplete PM model by introducing a failure intensity increment factor and an age reduction factor and establish a maintenance strategy optimization model with comprehensive consideration of reliability and cost-effectiveness ratio. Finally, the validity of the model in this paper is verified by arithmetic case analysis. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0153/

Failure evaluation of outdoor polymeric insulators based on statistical condition-assessment methods

Field aging of outdoor polymeric insulators results in unscheduled outages in power systems particularly in harsh climate conditions. In this paper, a strategic maintenance framework is proposed in order to prevent overhead line outages. The presented scheme is based on condition assessment of the real aged polymeric insulators. Data of tensile test, hardness test, thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) are used to extract critical features of the aged samples based on classification of fuzzy c-means clustering. Then, a lifetime prediction analysis is done based on Cox hazards analysis to develop further maintenance strategy. The proposed model is validated through numerical analyses on polymeric insulators for various validation evaluations including pollution severity analysis and hydrophobicity class (HC) measurement. Obtained results verify accuracy of the intelligent model to predict lifetime of the aged polymeric insulators. The presented strategic framework can be effectively applied to online monitoring, condition assessment and preventive maintenance programs of the power system utilities.

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.

A bi-population cooperative scatter search algorithm for distributed hybrid flow shop scheduling with machine breakdown

The occurrence of machine breakdowns is a frequent and dynamic phenomenon in the production process. The implementation of effective preventive measures can mitigate such events and result in reduced production costs. This paper investigates the distributed hybrid flow shop scheduling problem with machine breakdown (DHFSSPB) considering short maintenance time. The bi-population cooperative scatter search (BCSS) algorithm is proposed to address the DHFSSPB, wherein the search for the optimal scheduling sequence is transformed into genetic evolution aiming to obtain a gene chain with both minimum lower bound and minimum cost attributes. Firstly, the DHFSSPB problem is modeled through a combination of predictive maintenance strategy and right-shift rescheduling rule. Subsequently, a diversification approach is developed to facilitate attribute inheritance, enhance the efficiency of job allocation, and establish a reference set. The reference set is partitioned into two subpopulations based on lower bound attributes and cost attributes, respectively. The corresponding hybrid search strategies are designed to enhance the efficiency of job sorting and machine selection for subpopulations with distinct attributes. The cooperative evolution between subpopulations occurs through the competitive interaction and fusion of individuals. An enhanced reinforcement learning approach is proposed to expedite the acceleration of individual attribute evolution by leveraging evolutionary knowledge acquired from populations, thereby effectively guiding the evolutionary trajectories of individuals. Additionally, a method for evaluating the population during the learning process is developed based on problem characteristics to enhance learning efficiency. Experimental results demonstrate that BCSS outperforms the comparative algorithm in solving the DHFSSPB.

Reliability modeling and optimal maintenance strategies for stochastically deteriorating systems with random degradation latency

Reliability modeling and optimal maintenance strategies for stochastically deteriorating systems with random degradation latency

Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform

ABSTRACTPhotovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development.

Preventive Maintenance Strategy Prediction of the Firewater Systems Based on the Pythagorean Fuzzy Cost–Benefit–Safety Analysis and Fuzzy Dematel

The firewater system is a complex system associated with the safety process of Hydrogen storage tanks. Predicting preventive maintenance strategies is essential to ensure the long-term reliability of this system. Therefore, it is necessary to evaluate the multistate reliability of the firewater system in order to predict preventive maintenance strategies and provide safety measures. A polymorphic fuzzy fault tree analysis (PFFTA) for the risk analysis of complex systems has attracted much attention because of its powerful evaluation capability and its ability to analyze relationships among basic events. However, obtaining multistate failure probability (MFP) data for basic events in PFFTA has always been a major challenge. It is also difficult to quantify the minimum cut set (MCS) in PFFTA and determine the critical components for selecting a preventive maintenance strategy. In this study, we propose the Pythagorean fuzzy cost–benefit–safety analysis by using the PFFTA, an improved consistency aggregation method (I-CAM), and fuzzy Dematel for a predictive preventive maintenance strategy. In the proposed approach, the I-CAM method was used to collect and aggregate weights of experts’ opinions to evaluate the MFP of basic events in PFFTA. As a result, a triptych cost–benefit–safety analysis based on Pythagorean fuzzy sets (PFSs) and the sum-product method (SPM) was estimated to reduce expert subjectivity, support an improved cost-effectiveness index to rank critical components, and fuzzy Dematel to evaluate influence of proposed preventive maintenance actions. To clarify the effectiveness and feasibility of the proposed methodology, a case study of the firewater system related to the plant is located in SONELGAZ electricity power plant (OUMACHE Unit) was demonstrated. Both evaluations of the cost–benefit–safety analysis of the critical component were performed, and selected the influence of preventive maintenance strategy of the firewater system was predicted.

Maintenance Challenges in Maritime Environments and the Impact on Urban Mobility: Machico Stayed Bridge

This article investigates the challenges of maintaining the Machico Cable-Stayed Bridge in a marine environment, focusing on its implications for urban mobility. The primary problem addressed is the impact of harsh marine conditions on the structural integrity of the bridge, which poses significant challenges for ongoing maintenance and safety. The research highlights unique aspects such as the effects of saltwater exposure on materials and the interplay between infrastructure and urban transit dynamics. By emphasizing these critical issues, this study aims to provide insights into effective maintenance strategies and contribute to the broader discourse on urban mobility in coastal regions.

Enhanced Method for Localization of Partial Discharges in Oil-Filled Transformers Using Acoustic Emission Signals

The accurate measurement of partial discharges (PD) in power transformers is a critical component in identifying faults and planning effective maintenance strategies. Acoustic emission (AE) sensing technology is suitable for detecting partial discharges in transformers because it is less affected by external electromagnetic interference and detects PD non-invasively. This paper delves into examining the relationship between the detection intensity, the specific type of PD source, and the distance to the discharge source, using AE sensors. It was observed that AE wave intensities from corona discharges tend to be relatively strong, indicating a higher detection probability. Creepage discharges usually exhibit the next level of intensity, followed by the PD occurring in bubbles. It is considered that the intensity of the AE waves varies significantly depending on both the speed at which the discharge propagates and the medium and volume of the discharge space. Furthermore, this study has conducted experimental comparisons among three distinct methods for calculating the Time Difference of Arrival (TDOA) in localization calculations. These methods are the energy criterion, Generalized Cross-Correlation (GCC), and GCC with Phase Transformation (PHAT). The experimental results suggest that the energy criterion method is particularly effective when sensors are distributed placement around the entire tank. In contrast, the GCC-PHAT method shows greater suitability in scenarios where sensors are centralized placement in certain sections of the tank. It was clearly observed that the GCC-PHAT method, which suppresses the impact of noise and reflections, consistently achieves higher estimation accuracy in comparison to the standard GCC method. Notably, this method has shown the ability to maintain its accuracy levels even in cases of low discharge intensities. The implications of these findings are significant, as they could improve the precision and effectiveness of maintenance diagnostics in power transformers.

Thermographic Analysis of Green Wall and Green Roof Plant Types under Levels of Water Stress

Urban green infrastructure (UGI) plays a vital role in mitigating climate change risks, including urban development-induced warming. The effective maintenance and monitoring of UGI are essential for detecting early signs of water stress and preventing potential fire hazards. Recent research shows that plants close their stomata under limited soil moisture availability, leading to an increase in leaf temperature. Multi-spectral cameras can detect thermal differentiation during periods of water stress and well-watered conditions. This paper examines the thermography of five characteristic green wall and green roof plant types (Pachysandra terminalis, Lonicera nit. Hohenheimer, Rubus tricolor, Liriope muscari Big Blue, and Hedera algeriensis Bellecour) under different levels of water stress compared to a well-watered reference group measured by thermal cameras. The experiment consists of a (1) pre-test experiment identifying the suitable number of days to create three different levels of water stress, and (2) the main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages. The thermal images were captured analyzed from three different types of green infrastructure. The method was suitable to detect temperature differences between plant types, between levels of water stress, and between GI types. The results show that leaf temperatures were approximately 1–3 °C warmer for water-stressed plants on the green walls, and around 3–6 °C warmer on the green roof compared to reference plants with differences among plant types. These insights are particularly relevant for UGI maintenance strategies and regulations, offering valuable information for sustainable urban planning.

Comparative analysis of response surface methodology and adaptive neuro-fuzzy inference system for predictive fault detection and optimization in beverage industry

Maintenance is crucial for ensuring equipment reliability and minimizing downtime while managing associated costs. This study investigates a data-driven approach to predicting machine faults using Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). RSM was employed to develop a mathematical model to analyze how operational parameters such as pressure, voltage, current, vibration, and temperature affect fault occurrence. Data were collected at three levels for each parameter using a central composite design. The model identified that faults peaked at a pressure of 28.38 N/m2, an operating voltage of 431.77 V, current consumption of 12.54 A, machine vibration of 47.17 Hz, and temperature of 25°C, with a maximum of 25 faults observed. Conversely, the lowest fault detection occurred at a pressure of 29.42 N/m2, an operating voltage of 441.04 V, current consumption of 12.04 A, machine vibration of 49.46 Hz, and temperature of 46.5°C. A strong correlation was found between these parameters and machine faults, with the model achieving high accuracy (R2 = 98.22%) and statistical significance (p-value <0.05), demonstrating its reliability in predicting faults. The study also compared RSM with ANFIS for fault detection and process optimization in the beverage industry. While RSM effectively optimized parameter relationships, ANFIS, with its adaptive learning capabilities, provided superior fault prediction accuracy. This comparative analysis highlighted the strengths of both methods and suggested that integrating them could enhance predictive maintenance strategies. The findings offer valuable insights for industry practitioners, recommending a combined approach to improve fault detection, optimize production processes, and enhance operational efficiency.

Characterization of the remaining material and mechanical properties of historic wooden foundation piles in Amsterdam

The majority of the bridges in the historic city centre of Amsterdam are supported by wooden foundation piles. Most of these were constructed 100–300 years ago, currently raising concerns about potential safety issues. The wooden piles under the bridges remain entirely under the water table, potentially subjected to bacterial decay in anaerobic conditions. Bacterial degradation proceeds at a slow rate, allowing the piles to perform their function for many years, although causing a reduction of their load-carrying capacity over time. To this end, a large experimental campaign was conducted to characterize the material and mechanical properties in relation to biological decay of 60 spruce and fir piles, dated back to 1727, 1886 and 1922, retrieved from two bridges in Amsterdam. Large-scale compression tests were carried out on 201 pile-segments extracted from head, middle-part and tip of the piles to determine their remaining short-term compressive strength. Micro-drilling measurements were conducted on each pile and analysed with a TU-Delft-developed algorithm, aimed at determining the soft shell – the width of the decayed outer layer of the piles’ cross section. Micro-drilling allowed to accurately assess the remaining sound cross section of the pile, which resulted to be well correlated to its mechanical properties. The extent of decay throughout the cross-section of the piles was assessed within sapwood and heartwood through experimental models from literature and validated with Computed Tomography (CT) scanning. This allowed to identify that bacterial decay was only present in the non-durable sapwood, even in very degraded piles. Moreover, the soft shell resulted to be rather uniform along the piles’ length. The analysis of decay, supported by micro-drilling and CT scans, allowed to develop experimental equations to predict the remaining short-term compressive strength along the pile length. The micro-drilling technique is now used on a large scale in Amsterdam, supporting the assessment of the remaining load carrying-capacity of wooden foundation piles in the city, aiding in the planning of conservation, maintenance and preservation strategies.

Digital twin-enhanced opportunistic maintenance of smart microgrids based on the risk importance measure

Smart microgrids face more diverse and frequent risks than traditional grids due to their complexity and reliance on distributed generation. Ensuring the reliable operation of smart microgrids requires effective maintenance. Traditional maintenance methods, based on periodic inspections and fault diagnosis, struggle to adapt to the dynamics and complexity of microgrid systems. The introduction of digital twin technology provides a new solution for the opportunistic maintenance of microgrid systems. This paper presents a digital twin microgrid architecture for real-time monitoring and decision-making in opportunistic maintenance. Meanwhile, this paper introduces a risk importance measure to aid to optimize opportunistic maintenance strategies when resources are limited. Finally, a wind-solar-storage microgrid is used to illustrate the proposed method. Experimental results show that the proposed method significantly reduces maintenance costs and improves system reliability, effectively supporting microgrid maintenance.