Search Results (44)

As electric multiple unit (EMU) operations and maintenance demands have expanded, spare parts supply chain management has become increasingly crucial. This study emphasizes the supply challenges of EMU spare parts, including inadequate minimum inventory levels and prolonged response times. Redesigning the OEM–railway bureau vendor-managed inventory (VMI) model contract incentive and penalty system is the key goal. Connecting the spare parts supply system with its characteristics yields a game theory model. This study analyzes and compares the equilibrium strategies and profits of supply chain members under different mechanisms for managing critical spare parts. The findings demonstrate that mechanism contracts can enhance supply chain performance in a Pareto-improving manner. An in-depth analysis of downtime loss costs, procurement challenges, and order losses reveals their effects on supply chain coordination and profit allocation, providing railway bureaus and OEMs with a theoretical framework for supply chain decision-making. This study offers theoretical justification and a framework for decision-making on cooperation between OEMs and railroad bureaus in the management of spare parts supply chains, particularly for extensive EMU operations. Full article

The implementation of a system for the acquisition, transferring and processing of data essential for marine engine diagnostics is the basis of condition maintenance. Determining the most influential operating parameters, and conducting monitoring, analysis and taking action based on expert knowledge prevents downtime due to possible malfunctions. Timely corrections and replacements of worn parts based on condition diagnostics enable maintenance planning, which reduces the frequency of maintenance and the accumulation of unnecessary spare parts in warehouses. For research purposes, a system for remote data collection from electronically controlled marine engines was developed and applied. The system was installed on a four-stroke high-speed propulsion engine from a ferry, and the operating parameters of the engine were monitored during regular sailing in order to detect irregularities and possible failures at an early stage. The measurement system monitored the parameters obtained through the electronic engine control module via the J1939 protocol, and in this paper, the following relevant engine parameters were analyzed: engine speed, boost pressure, fuel consumption and engine load at the current speed. The analysis included the creation of trend diagrams to present the distribution of the minimum, median and maximum values of each parameter of all the measurements performed. This study also examined the simulation of the faults of the high-speed four-stroke marine engine model. By utilizing sensor data from critical system components, this research investigated different scenarios. The analysis aimed to elucidate the impact of these faults on engine performance. Based on the analyses of the relevant operating parameters of the engine, diagnostics were carried out. Full article

Health management is the foothold of remaining useful life (RUL) prediction, known as ‘prognostics’. However, sudden failures in complex systems can lead to increased downtime and maintenance costs, ultimately diminishing system health and availability. Considering intelligent prognostics of components, maintenance decision-making for spare parts ordering and replacement is proposed within a spare parts support system. The decision-making process aims to minimize costs while maximizing availability as its primary objective. It considers spare parts ordering time and replacement time as key decision variables. By developing a maintenance decision-making model, it aims to determine the optimal time for ordering and replacing spare parts. This maintenance approach is designed to provide technical support for effective and rational equipment management decision-making. Full article

Spare parts management is a critical aspect of high-speed train health management, playing a vital role in maximizing in-service time and minimizing maintenance costs. However, traditional spare parts management methods, which rely solely on historical experience and suggest spare parts quantities or ratios in equipment manuals, often lack practicality and fail to meet real-world demands. To address these limitations, this paper proposes a performance prediction-based spare parts management strategy for high-speed trains. The strategy comprises three main components. First, a performance degradation model is developed using performance evaluation results to define a performance degradation envelope. Next, the required quantity or ratio of spare parts for multiple devices in different performance states is determined using the expected performance score method. Finally, the timing of spare parts orders is scientifically optimized by accounting for production and transportation lead times. To demonstrate the effectiveness of the proposed strategy, we conducted experiments using the spare parts management of a specific high-speed train running gear as a case study and compared it with existing spare parts management methods. Full article

Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought. To assess vegetation drought, four indices were used: Vegetation Health Index (VHI), Vegetation Drought Index (VDI), Visible Infrared Drought Index (VSDI), and Temperature Vegetation Drought Index (TVDI). To validate vegetation drought indices, they were compared with Global Land Data Assimilation System (GLDAS) precipitation data. The vegetation indices showed a strong, statistically significant correlation with GLDAS precipitation data in most regions of the province. Among all indices, the VHI showed the highest correlation with precipitation (moderate (0.3–0.7) in 51.7% and strong (≥0.7) in 45.82% of lands). The output of vegetation indices revealed that the study province has experienced widespread drought in recent years. The results showed that the southern and central regions of the province have faced more severe drought classes. In the second part of this research, hydrological drought monitoring was conducted in fifty third-order sub-basins located within the study province using the Total Water Storage (TWS) deficit, Drought Severity, and Total Storage Deficit Index (TSDI Index). Annual average calculations of the TWS deficit over the period from April 2012 to 2016 indicated a substantial depletion of groundwater reserves in the province, amounting to a cumulative loss of 12.2 km³ Analysis results indicate that drought severity continuously increased in all study basins until the end of the study period. Studies have shown that all the studied basins are facing severe and prolonged water scarcity. Among the 50 studied basins, the Rahmatabad basin, located in the semi-arid northern regions of the province, has experienced the most severe drought. This basin has experienced five drought events, particularly one lasting 89 consecutive months and causing a reduction of more than 665.99 km³. of water in month 1, placing it in a critical condition. On the other hand, the Niskoofan Chabahar basin, located in the tropical southern part of the province near the Sea of Oman, has experienced the lowest reduction in water volume with 10 drought events and a decrease of approximately 111.214 km³. in month 1. However, even this basin has not been spared from prolonged droughts. Analysis of drought index graphs across different severity classes confirmed that all watersheds experienced drought conditions, particularly in the later years of this period. Data analysis revealed a severe water crisis in the province. Urgent and coordinated actions are needed to address this challenge. Transitioning to drought-resistant crops, enhancing irrigation efficiency, and securing water rights are essential steps towards a sustainable future. Full article

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices. Full article

The presented contribution deals with the research of the maintenance strategy and procedures for improving maintenance processes in order to increase the efficiency and quality of the production system. It is based on a thorough analysis of the research of the available literary sources published in foreign and domestic scientific journals. The subsequent proposal includes defining new goals and maintenance performance indicators relevant to today’s production systems to track improvements in the sustainable development of the production system. There are also basic principles of the maintenance strategy with links to the production system and the choice of strategy for the organization. This paper emphasizes the audit of maintenance management and the implementation of quality in maintenance. Next, a new procedure for changing the maintenance strategy is described. This process includes reviewing the criticality of machines and equipment and their structural units, then resource and capacity planning and inputs for maintenance management, and the impact of maintenance on the operating costs of the production system. This was based on which partial projects in companies were verified—automotive industry (spare parts, preventive maintenance, planned maintenance, RCFA, TPM), rubber industry (quality, production efficiency), pharmaceutical industry (preventive and predictive maintenance), engineering industry (TPM, LOTO, RCM). The overall verification of the creation of the maintenance strategy and the proposed methodology was carried out on the basis of the outputs of the sub-projects and overall projects in the following companies with positive results—glass industry, chemical industry, and operational research (research and development of equipment for non-reactor parts of nuclear power plants). Ten steps of the audit of the current state of the management of maintenance processes were proposed, to ensure economic improvements in the costs of maintenance processes and operating costs, ensuring competitiveness. A methodology for changing the maintenance strategy focused on the efficiency, quality, and costs of the production system was proposed. The average benefits from the implementation of strategy changes in organizations reached at least the following: (1) increase in production efficiency—OEE (7%), (2) improvement in production quality (20%), (3) improvement in performance (15%), and (4) reduction in maintenance process costs (10%) in implemented projects. Full article

Technological equipment in quarries that extract and deliver aggregates for different uses operates in a predetermined flow depending on the type of rocks exploited and the dimensional characteristics imposed on the final products. In this context, the interruptions in operation required to replace high-wear parts (such as the teeth of excavators and bucket loaders) must be limited as much as possible through technological solutions to increase their service life. The evolution of the wear of the teeth of the quarry equipment that come into direct contact with rocks was concretely established in the production process, in parallel with the wear values obtained by simulating the wear phenomenon in laboratory conditions, in order to validate the data collected during the operation of the machines. Preventive–repetitive maintenance within the activities of reconditioning the worn surfaces of the teeth, through the charging process by manual electric welding with covered electrodes, was applied directly to the machine, which led to the shortening of the interruptions in operation necessary to replace these spare parts. Full article

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant Pythagorean fuzzy sets and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, the basic definitions of Type-2 hesitant Pythagorean fuzzy sets were given, which were further promoted to Type-n hesitant Pythagorean fuzzy sets, and the basic order relation criterion of Type-2 hesitant Pythagorean fuzzy sets was introduced. Second, a complete evaluation system for spare parts supply support capacity was established with the spare parts of civil aircrafts as the study objects, and each evaluation indicator was introduced in detail. Then, the spare parts support solutions were preferentially sorted using the correlation coefficient formula of Type-2 hesitant Pythagorean fuzzy sets and improved TOPSIS. Finally, the reliability and reasonability of the proposed method were verified through an example calculation and comparative analysis. The experimental results indicate that the proposed method can acquire the evaluation results of spare parts support capacity more scientifically and can be referenced by relevant studies. Full article

High temperature superconductors (HTSs) are enablers of extensive electrification for aircraft propulsion. Indeed, if used in electrical machines, HTS materials can drastically improve their performance in terms of the power-to-weight ratio. Among the different topologies of superconducting electrical machines, a flux modulation machine based on HTS bulks is of interest for its compactness and light weight. Such a machine is proposed in the FROST (Flux-barrier Rotating Superconducting Topology) project led by Airbus to develop new technologies as part of their decarbonization goals driven by international policies. The rotor of the machine will house large ring-segment-shaped HTS bulks in order to increase the output power. However, the properties of those bulks are scarcely known and have barely been investigated in the literature. In this context, the present work aims to fill out partially this scarcity within the framework of FROST. Thus, a thorough characterisation of the performances and homogeneity of 11 large REBaCuO bulks was carried out. Ten of the bulks are to be utilized in the machine prototype, originally keeping the eleventh bulk as a spare. A first set of characterisation was conducted on the eleven bulks. For this set, the trapped field mapping and the critical current were estimated. Then, a series of in-depth characterisations on the eleventh bulk followed. It included critical current measurement, X-ray diffraction, and scanning electron microscopy on different millimetre-size samples cut out from the bulk at various locations. The X-ray diffraction and scanning electron microscopy showed weakly oxygenated regions inside the bulk explaining the local drop or loss in superconducting properties. The objective was to determine the causes of the inhomogeneities found in the trapped field measured on all the bulks, sacrificing one of them, here the spare one. To help obtain a clearer picture, a numerical model was then elaborated to reproduce the field map of the eleventh bulk using the experimental data obtained from the characterisation of its various small samples. It is concluded that further characterisations, including the statistics on various bulks, are still needed to understand the underlying reasons for inhomogeneity in the trapped field. Nonetheless, all the bulks presented enough current density to be usable in the construction of the proposed machine. Full article

Online-to-offline (O2O) retailing offers unique opportunities for customizable assembled products with spare parts. Customers can browse and configure their desired product online, selecting from various components. Imperfect production, where a certain percentage of products have defects, can be amplified in the manufacturing system. Stricter carbon emission regulations put pressure on manufacturers to minimize waste. This creates a tension between discarding imperfect products, generating emissions, and potentially offering them at a discount through the O2O channel, which could raise quality concerns for consumers. In this study, an imperfect single-stage production process is examined, incorporating manufacturing–remanufacturing within a single stage for assembled products containing various spare parts. The study explores an investment scenario aimed at enhancing the environmental sustainability of the product. Additionally, two carbon emissions regulation strategies, specifically carbon cap-and-trade regulation and carbon taxation, are evaluated for their effectiveness in mitigating carbon footprints. The identification of waste, particularly in the form of defective items, is achieved through automated inspection techniques. The demand for spare parts associated with the assembled products is intricately linked to the selling prices set across diverse channels. Finally, the total profit of the manufacturing system is maximized with the optimized value of the selling prices, order quantity, backorder quantity, and investments in autonomated inspection, setup cost, and green technology. Numerical illustrations show that system profit was optimized when the defective rate followed a triangular distribution under carbon cap-and-trade regulation and when green technology investment helped to enhance retailer profit by $18.12\%$, whereas autonomated inspection increased retailer profit by $10.27\%$. Full article

For years, gears have been self-made by many industrial plants as substitutes (custom spare parts) for original parts from the manufacturer. This common practice uses a process called reverse engineering (RE). However, in the available scientific database, it is difficult to find articles about the accuracy of such a process. And while it is obvious that in order to obtain the most accurate quality of such a process, modern measurement techniques (coordinate, optical) should be used, most companies cannot afford to purchase such equipment. Reproducing gear geometry is difficult. But the issue of RE of non-standard gears seems to be even more difficult. This is why the authors undertook pilot studies to assess the accuracy of the RE process of worn, non-standard spur gears using conventional techniques and measuring instruments. Eight gears were tested, the module of which ranges from 1.020 to 4.98 mm. The key parameter was selected to estimate the accuracy of the process—the base pitch. The goal is to determine the value of the profile angle. Eleven models were proposed to estimate the nominal tolerance field, using various types of random data distribution. The tested gears were made in IT grade: 6, 7, 8, and 9 according to DIN 3961. Vernier disk micrometers were used for research. It has been shown that the nominal module does not have to be treated as a random variable in the population. Equation of identity was developed, allowing conversion of any gear with specific values of geometric parameters into an identical gear with alternative values of these parameters. The most effective estimating model was selected taking into account the symmetric Student–Fisher distribution with a confidence level of 60%. However, it is not possible to correctly reproduce the geometry of the gear wheel in that way. The following aspects should be taken into account: type and degree of mode of failure, number of load cycles, rotational speed, direction of rotation, material, type of thermochemical treatment, and torque. A simulation using FEM should be performed to determine the fatigue plastic deformations and diagnose their impact on the geometric dimensions of the gear wheel. Full article

Access to spare parts in the maritime industry is limited throughout most of a ship’s life cycle. The limitation is caused by both the geographical distance of vessels from suppliers and the often limited turnaround time during which parts can be delivered. Manufacturing some parts onboard is possible, but it is a time-consuming and labour-intensive process. Advanced manufacturing techniques could be used to improve access to spare parts at sea by combining the desirable materials properties and flexibility of Direct Energy Deposition (DED) and the higher dimensional tolerances of Computer Numerical Control (CNC) manufacturing. The present study assesses the comparative viability of onboard implementation of advanced manufacturing techniques for offshore assets as a capital investment in different modes against an option of no onboard advanced manufacturing using a multi-criteria decision analysis method. To this end, a Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) is employed considering the techno-economic and environmental aspects of the decision-making process as well as the inherent challenges that come with a new area of research. Finally, the challenges, opportunities, and pathways to onboard maintenance using additive manufacturing are discussed within the scope of the sustainable future for ship and offshore energy assets. Full article

The quick development of machine learning techniques provides a superior capability for manufacturing enterprises to make effective decisions about inventory management based on spare parts demand (SPD) data. Since SPD sequences in practical maintenance applications usually show an intermittent distribution, it is not easy to represent the demand pattern of such sequences. Meanwhile, there are some aspects like manual report errors, environmental interference, sudden project changes, etc., that bring large and unexpected fluctuations to SPD sequences, i.e., anomalous demands. The inventory decision made based on the SPD sequences with anomalous demands is not trusted by enterprise engineers. For such SPD data, there are two great concerns, i.e., false alarms in which sparse demands are recognized to be anomalous and missing alarms in which the anomalous demands are categorized as normal due to their adjacent demands having extreme values. To address these concerns, a new unsupervised anomaly-detection method for intermittent time series is proposed based on a dual-tailed probability. First, the multi-way delay embedding transform (MDT) was applied on the raw SPD sequences to obtain higher-order tensors. Through Tucker tensor decomposition, the disturbance of extreme demands can be effectively reduced. For the reconstructed SPD sequences, then, the tail probability at each time point, as well as the empirical cumulative distribution function were calculated based on the probability of the demand occurrence. Second, to lessen the disturbance of sparse demand, the non-zero demand sequence was distilled from the raw SPD sequence, with the tail probability at each time point being calculated. Finally, the obtained dual-tailed probabilities were fused to determine the anomalous degree of each demand. The proposed method was validated on the two actual SPD datasets, which were collected from a large engineering manufacturing enterprise and a large vehicle manufacturing enterprise in China, respectively. The results demonstrated that the proposed method can effectively lower the false alarm rate and missing alarm rate with no supervised information provided. The detection results were trustworthy enough and, more importantly, computationally inexpensive, showing significant applicability to large-scale after-sales parts management. Full article

The optimization of preventive maintenance and spare part ordering strategies for modern production equipment is of utmost importance, given its substantial influence on the reliability of equipment systems. Furthermore, the optimization problem discussed here has a direct impact on the reduction of maintenance expenses, thus making it a significant area of research. The optimization of preventive maintenance and spare parts ordering techniques for contemporary industrial equipment, which is a massive and complex system, faces substantial obstacles notwithstanding prior research efforts in the subject. Prior studies have typically assumed a fixed lead time for spare parts ordering, often leading to discrepancies with actual practice. When faced with a critical component failure, such as rolling bearings, it is not advisable for the decision maker to strictly adhere to the ordering strategy. Therefore, this paper presents a novel approach to the maintenance management method, which optimizes preventive maintenance and spare parts ordering strategies using a dynamic early warning period model based on different equipment states. The model incorporates two maintenance approaches, namely normal ordering and emergency ordering, and the equipment will adopt the corresponding maintenance method according to its state. Furthermore, the model takes imperfect detection of equipment states into account since equipment monitoring is not always accurate. Numerical experiments were conducted using rolling bearings, which are a crucial component in typical mechanical equipment, as a case study. The findings indicate that the improved model exhibits a unit time cost of 1.3021, whereas the original model has a unit time cost of 1.3611. Consequently, the new model effectively reduces the maintenance cost. This new method can better resolve the coordination challenge between preventive maintenance and spare parts ordering for equipment, the enhancing of equipment system reliability, and reduce maintenance expenses. In summary, the text presents a significant contribution in the form of a proposed preventive maintenance model that offers increased flexibility, aiming to effectively reduce maintenance costs. Full article