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In this paper, the reliability allocation and index of overall cost of rotary ultrasonic vibration-assisted EDM machine tool were studied. The failure rate of each module of the rotary ultrasonic vibration-assisted EDM machine tool can be accurately predicted by the maximum entropy ordered weighted average algorithm so that the corrected maintenance cost can be predicted. And effective control of preventive maintenance costs can be achieved by selecting the best maintenance times under the premise of reliability. The cost of corrective maintenance can be reduced by improving modules with a high failure rate, so as to realize the purpose of cost optimization under the premise of determining the maintenance time and reliability. The reliability function of the rotary ultrasonic vibration-assisted EDM machine tool is established according to the series-parallel relationship of each module. The results showed that the reliability of rotary ultrasonic vibration-assisted EDM machine tool is stabler at the non-fixed maintenance period according to the comparison between the non-fixed and the fixed maintenance period.

ABSTRACT Effective maintenance strategies that balance system performance and robustness are crucial for both public and private enterprises. In response to increasing competition and the high cost of infrastructure and equipment, decision-makers are increasingly relying on mathematical modeling to support optimization and resilience. This paper introduces an adaptation criterion for selecting the optimal maintenance policy by simultaneously evaluating performance and robustness, replacing the conventional cost-based model with a Robust Quantification Model (RQM). Our proposed economic criterion is a linear combination of three key elements: the asymptotic average maintenance cost per unit of time (C∞), the standard deviation (σ) of the maintenance cost per replacement cycle (MCPRC), which measures the variability of total maintenance costs over each renewal cycle, and the weight parameter (λ) reflecting the trade-off between cost performance and robustness. This combined criterion allows for a joint assessment of stability and economic efficiency. We evaluate the three techniques (Block Replacement strategy (BR), Periodic Inspection Replacement strategy (PIR) and Quantile-based Inspection Replacement strategy (QIR)) using the proposed robust economic criterion, and compare their effectiveness under uncertain system degradation modeled using a homogeneous Gamma process. Monte Carlo simulations are employed to estimate the expected cost and variability across multiple degradation scenarios, ensuring empirical accuracy of the proposed criterion. The simulation results demonstrate that the proposed criterion effectively captures both the average cost behavior and its variability, providing a comprehensive tool for selecting resilient and cost-effective maintenance strategies in degrading systems.

Power generation from wind farms is growing rapidly around the world. In the past decade, wind energy has played an important role in contributing to sustainable development. However, wind turbines are extremely susceptible to component damage under complex environments and over long-term operational cycles, which directly affects their maintenance, reliability, and operating costs. It is crucial to realize efficient early warning of wind turbine failure to avoid equipment breakdown, to prolong the service life of wind turbines, and to maximize the revenue and efficiency of wind power projects. For this purpose, wind turbines are used as the research object. Firstly, this paper outlines the main components and failure mechanisms of wind turbines and analyzes the causes of equipment failure. Secondly, a brief analysis of the cost of wind power projects based on equipment failure is presented. Thirdly, the current key technologies for intelligent operation and maintenance (O&M) in the wind power industry are discussed, and the key research on decision support systems, fault diagnosis models, and life-cycle costs is presented. Finally, current challenges and future development directions are summarized.

Within the subject area of maintenance and maintenance management, authors identified a deficiency in studies focussing on the expected value from adopting predictive maintenance (PdM) techniques for machine tools (MTs). Authors identified no studies focussing on presenting a PdM value analysis or cost model specifically for small-medium enterprises (SMEs) operating computer numerically controlled (CNC) MTs. This paper’s novelty is addressing SMEs’ minimal representation in literature by explanatorily collecting data from SMEs within the focal area via surveys, modelling and analysing datasets, then proposes a cost-effective PdM system architecture for SME CNC machine shops that predicts cost savings ranging from £22,804 to £48,585 over a range of 1–50 CNC MTs maintained on a distributed numerically controlled (DNC) network. It affirms PdM’s tangible value creation for SME CNC machine shops with predicted positive impacts on their MT cost and performance drivers. These exploratory research findings corroborate SMEs pooling together to optimise their CNC MT maintenance cost through the recommended system architecture. Finally, it introduces opportunities for further PdM research taking into account SMEs’ perspective. The paper’s industrial application is confirmed from the surveyed SMEs that demonstrated their current utility of PdM; then anonymous positive feedback on the online dashboard, shared with participant companies, confirmed the research results supported SMEs in considering exploring the path to adapting PdM. It is anticipated that beneficiaries of this research will be maintenance managers, business executives and researchers who seek to understand the expected financial and performance impact of adopting PdM for a MT’s overall life cycle costs.

Civil aircraft maintenance cost is an important indicator to measure the maintainability and economy of civil aircraft, and it accounts for a considerable proportion of aircraft operating costs. Data mining technology can unearth potentially high-value data from massive aircraft operating data. In this paper, aiming at the problem that there are very few actual aircraft maintenance data that can be effectively used in the development stage of civil aircraft, the research on the direct maintenance cost analysis and optimization method of civil aircraft based on data mining is carried out. The theoretical basis of direct maintenance cost analysis based on data mining is studied, the factors affecting the direct maintenance cost of civil aircraft are analyzed based on data mining, and the DMC analysis method is established. On the basis of the above research, the direct maintenance cost analysis model of civil aircraft was established and optimized to achieve the goal of reducing direct maintenance costs. It is of great significance for reducing aircraft life cycle costs and improving aircraft market competitiveness.

Contemporary industrial equipment is increasingly developing towards complexity. In order to ensure the high reliability and sustainability of industrial equipment, more flexible maintenance strategies have attracted extensive attention. In view of this, this paper aims to summarize the current situation of existing maintenance strategies, so as to enable colleagues in the industry to choose or formulate more efficient maintenance strategies. Firstly, the characteristics, application potential and limitations of single component maintenance strategies, such as corrective maintenance, preventive maintenance and predictive maintenance, are described in detail from the perspective of maintenance time. On the basis of single component maintenance and the dependency between multiple components, the advantages and disadvantages of multicomponent maintenance strategies, such as batch maintenance, opportunity maintenance and group maintenance, are summarized, and suggestions for the future maintenance of industrial equipment are proposed. Based on this, industries can select the appropriate maintenance strategy according to their equipment characteristics, or improve their existing maintenance strategies based on actual needs.

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis.

At present, the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance, breakdown maintenance, and condition-based maintenance, which is very likely to lead to over- or under-repair of equipment. Therefore, a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed. First, a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment, and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle. Then, based on the reliability as a constraint, the average maintenance cost and availability of the equipment are considered, and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs, each maintenance cycle and the replacement cycle of the PV power generation system components. Next, the inverter of a PV power plant is used as a research object. The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold, Through model comparison, when the optimal maintenance strategy is (0.80, 4), the average maintenance cost of this paper’s model are decreased by 20.3% and 5.54% and the availability is increased by 0.2395% and 0.0337%, respectively, compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold. Therefore, this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos.

A flexible manufacturing system (FMS) improves productivity and makes it more efficient. Maintaining reliability levels and reducing costs through proper maintenance strategies are key problems for the development and application of a FMS. This paper proposes a grouping preventive maintenance strategy of a FMS with optimized parameters by considering both reliability and cost. In this work, a three-layer evaluation index system is first presented to accurately estimate the reliability of the FMS; index weights of each layer were obtained by reliability importance modeling and analysis, considering maintenance strategies. An element-grouping preventive strategy is proposed based on an influencing analysis, and a parameter optimization problem (considering reliability and maintenance costs) was established. In this strategy, three maintenance methods are presented for the elements, including low-level maintenance with a large period, low-level maintenance with a small period, as well as the combination of low-level maintenance with a small period and high-level maintenance with a large period; the effects of reliability improvement of the elements on the subsystem’s reliability were analyzed to provide evidence for element grouping. Finally, the proposed method was applied to a box-part finishing FMS; the results indicate that this method can effectively reduce maintenance costs on the premise of satisfying the reliability requirements.