Maintenance Cost Analysis Research Articles

A Maintenance Cost and Work Performance Analysis on Irrigation Network at Kaubun Irrigation Area of East Kutai Regency, Indonesia

An irrigation network maintenance is an effort to maintain and secure the irrigation network to be always ready and well functioned. In facilitating its operational implementation and maintain its sustainability cycle, it can be attained through maintenance, repair, prevention, and security activities that must be conducted in continuity. The Kaubun irrigation area is one of the irrigation areas under the authority of central government with area length of 1,150 Ha (standard area) and 736 Ha (functional area), located in the East Kutai Regency. The Kaubun irrigation area has 1 (one) weir or dam and 2 (two) main canals. The irrigation network maintenance has not been carried out in routine and periodical ways either by the East Kalimantan Provincial Government or by the East Kutai Regency Government. Sediment accumulation is one of the main problems found in the secondary irrigation canal and able to cause canal blockage, reduced water flow capacity and increased infrastructure damage. This research was carried out by evaluating the Irrigation Network System and observing several factors causing channel damage with a guidance from regulation of Minister of Public Works and Public Housing Number:12/PRT/M/2015 on date 6 April 2015, regarding Exploitation and Maintenance of Irrigation Network. Result of the research showed that recapitulation of the work performance index for Kaubun Irrigation Network System was obtain the largest optimum value in Physical Infrastructure aspect with an index value of 29.01. Meanwhile, the result from main treatment priority by employing Analytic Hierarchy Process (AHP) method found the Physical Infrastructure aspect has a limiting value of 0.462. Finally, the routine and periodical maintenance costs for the Kaubun irrigation area is IDR.3.753.835.675.00

A Comprehensive Economic Analysis of Production and Maintenance Cost of Per Milch Animal Buffaloes of Ghazipur District of Uttar Pradesh, India

This study analyzes milk marketing in the Bhanwarkola and Ghazipur Sadar Blocks of Ghazipur District, Uttar Pradesh, focusing on the various channels for selling milk. It categorizes cattle owners, studies demographic patterns, and links these variables to milk marketing methods and efficiency. Using a descriptive and analytical design, the study divides cattle owners into small, medium, and large-scale enterprises. It evaluates demographic data such as age, gender, education, and social categories, as well as a thorough review of milk marketing methods and associated expenses. Data were gathered through biennial personal interviews done on a predetermined timetable, with an emphasis on production costs such as feed, fodder, labor, and animal care. The statistics suggest that the overall average production cost for desi buffaloes is Rs. 50,519.65 and Rs. 56,857.32 for murrah buffaloes, giving policymakers and dairy farmers vital insights into increasing profitability and sustainability.

Cost Studies of Reverse Osmosis Desalination Plants in the Range of 23,000–33,000 m3/day

The analysis of energy consumption in reverse osmosis desalination plants is the most important and relevant factor to study, because this parameter indicates the level of efficiency and competitiveness of the plant. The direct consequence of the high specific energy consumption (SEC) of a desalination plant in the production of water is one of the main obstacles to the exponential expansion of this technology worldwide. The methodological procedure used to carry out the work is based on the analysis of energy consumption, maintenance costs, staff, membranes, and reagents of three desalination plants with a production of more than 23,000 m3/day located in the Canary Islands (Spain); all data are obtained from real analyses collected “in situ” from 2015 to 2018. One of the main objectives of the current research on desalination plants is to reduce the SEC of seawater desalination plants (SWRO), incorporating energy recovery systems (ERS) and high efficiency pumps (HEP), and to implement different operational configurations with the aim of minimizing the energy requirements necessary to obtain a good product quality at minimum production cost.

A rate randomized geometric process with applications

AbstractThe geometric process has been widely studied in various disciplines and applied in reliability, maintenance and warranty cost analysis, among others. In its applications in maintenance policy optimisation, the geometric process assumes constant repair effectiveness by its process rate. Nevertheless, in practice, maintenance effectiveness may differ from time to time and can therefore be better depicted by a random variable. Motivated by this argument, this paper proposes a new variant of the geometric process, which is referred to as the rate randomized geometric process (RRGP). The probabilistic properties of the RRGP are then investigated. The maximum likelihood method is utilised to estimate the parameters of the RRGP. Numerical examples are given to show its applicability in both maintenance policy optimization and fitting real‐world failure datasets.

Investigation into Data Mining for Analysis and Optimization of Direct Maintenance Costs in Civil Aircraft Operations

Investigation into Data Mining for Analysis and Optimization of Direct Maintenance Costs in Civil Aircraft Operations

Statistical Analysis of Minor Highway Maintenance Costs

Statistical Analysis of Minor Highway Maintenance Costs

Linear accelerator maintenance cost analysis.

Medical linear accelerators are the most costly standard equipment used in radiation oncology, however the service costs for these machines are not well understood. With an increasing demand for linear accelerators due to a global increase in cancer incidence, it is important to understand the expected maintenance costs of a larger global installed base so that these costs can be incorporated into budgeting. The purpose of this investigation is to analyze the costs for medical linear accelerator service and maintenance at our institution, in order to estimate the service cost ratio. We collected the costs of parts used for all service work done on 32 medical linear accelerators over a two year period. The data was segregated by center, machine, linear accelerator type, and failure area in the machine. We found the service cost ratio (excluding software support expenses) to be 3.13% [2.74%, 3.52%,]. We observed a variability of parts costs, and overall variability of the service cost ratio to be between 2.14% and 5.25%. This result is lower than other estimates for service costs for medical equipment in general and medical linear accelerators specifically. Two-thirds of the service costs were due to labor costs, which indicate the importance of a well-trained service technician workforce. We estimated the service cost ratio for medical linear accelerators to be 3.13% [3.52%, 2.74%] of the initial capital cost. This result was lower than other estimates of the service cost ratio.

Analisis Penetapan Harga Sewa Tenant Serta Biaya Maintenance Untuk Menentukan Pendapatan Laba Menggunakan ROI Dan ROE Pada PT Priamanaya Jasa Boga Tahun 2019-2022

Priamanaya Jasa Boga is a company in the field of leasing places to trade food and beverages which is often referred to as a food court. To find out whether tenant rental prices and maintenance costs affect the profit income of PT. Priamanaya Jasa is seen from ROI and ROE, The method used in this study is Descriptive Quantitative. The results of this study show that the analysis of tenant rental pricing and maintenance costs to determine profit income using ROI and ROE at PT. Priamanaya Jasa Boga in 2019-2022 can get a profit or benefited.

FOREWORD FROM THE EDITOR-IN-CHIEF

Welcome to the first issue for the year 2023, a significant milestone for us as we celebrate the centennial of the Republic of Türkiye, the quarter-millennial anniversary of the Turkish Naval Academy (TNA), and the vicennial celebration of the Journal of Naval Sciences and Engineering (JNSE). These historical connections symbolize the remarkable progress achieved by our country, academy, and journal.
 
 In this issue, we are delighted to present five research papers that contribute to the advancement of naval sciences and engineering. These papers cover a wide range of topics, including the production of translucent alumina ceramics by tape casting, spectroscopic investigation of argon glow discharge in a plasma medium, thermal analysis of XLPE insulated submarine cables for different loading conditions, scale effects on the linear hydrodynamic coefficients of DARPA SUBOFF, and numerical investigation of HVAC systems in a naval ship compartment.
 
 We would also like to remind you that starting from September 1, 2022, we have been providing Digital Object Identifiers (DOIs) for accepted papers, ensuring their persistent identification. Additionally, we have implemented a new publishing process where papers are made available online before the printed hardcopy format, allowing for broader accessibility and faster dissemination of research findings.
 
 Our November 2023 issue is dedicated to highlighting cutting-edge advancements and research in naval technology and engineering. We have published a call for papers online, encouraging submissions on various topics, including 3D modeling, digital twin technology, IoT applications, 3D printing, virtual and augmented reality, high-performance computing, automation with robots, drones, and autonomous vehicles, shipyard construction and maintenance cost analysis, lean production methodologies, corrosion protection, wearable technologies for personnel monitoring, and big data analytics in preventive maintenance. We eagerly invite researchers and scholars to submit their work, contributing to this issue. The submitted papers in these areas will be given special attention alongside the journal's regular coverage, offering a comprehensive exploration of the latest advancements in naval sciences and engineering.
 
 We would like to extend our heartfelt gratitude to the authors for their valuable contributions to this issue, as well as our dedicated editorial and advisory board members for their expertise and guidance. We are also immensely grateful to the diligent reviewers who have generously contributed their time and expertise to ensure the quality and rigor of the papers published in this issue. Your insightful feedback and constructive criticism have played a vital role in shaping the content of this journal. 
 
 Additionally, we would like to express our appreciation to our readers for their continued support and trust in JNSE. Thank you all for your invaluable contributions.
 
 Fatih ERDEN, Ph.D.
 Editor-in-Chief

Research on Combination Optimization Method of Equipment Maintenance Task Package based on Maintenance Cost Analysis

This paper proposes a maintenance task package determination method based on the analysis of generalized maintenance cost (including maintenance cost, loss cost and maintenance time), aiming at the preventive maintenance task of aircraft. In the constructed maintenance task packaging model, practical issues such as approach, workload, logical sequence of maintenance tasks and the will of operators are considered in the implementation process of preventive maintenance tasks, to clarify the priority of packaging strategy and avoid problems such as excessively large workload distribution of each task package and even the inability to be realized in the actual field due to the pursuit of low cost. Based on the actual requirements of maintenance engineering, make preventive maintenance task package to meet the requirements of operators.

Analysis and Prediction of Differential Operation and Maintenance Cost of Power Transmission and Transformation

The operation and maintenance expenses of power transmission and transformation projects, as a significant power supply carrier of the nation, continue to rise as a result of the sustained and quick expansion of China's social economy and the quick growth of the country's power demand. Power grid businesses are under a lot of market pressure. To increase the level of lean management of the operation and maintenance costs of power transmission and transformation projects, power grid enterprises must significantly enhance their capacity to estimate the operation and maintenance costs of their organizations in advance. Machine learning algorithms are gradually applied to the operation and maintenance cost prediction of power transmission and transformation projects of power grid enterprises as a result of the ongoing development of big data technology, effectively increasing the accuracy of operation and maintenance cost prediction. In this paper, by analyzing the variables affecting the differential operation and maintenance cost of power transmission and transformation projects, a scientific and reasonable investment analysis model for the differential operation and maintenance cost of power transmission and transformation projects is constructed using the stochastic forest algorithm of particle swarm optimization, and the variables affecting the differential operation and maintenance cost of substations and transmission lines are obtained, which proves that the trend of the prediction model in this paper is more consistent with the actual situation.

Maintenance Strategy for Different Values of System Components

In this paper, the problem of the huge direct and indirect losses caused by vehicle failure is discussed. To solve the problem, we focus on the brake system of automobile as the research object and classification of high and low reliability value according to the characteristics of different components. On this basis, the maintenance strategy and cost analysis formula are given. Finally, the Monte Carlo method is used to simulate the cost calculation on MATLAB.

Economic analysis of operation and maintenance costs of hydropower plants

The world is experiencing deep climate changes caused by increased population and rapid urbanization. Hydropower is one of the renewable energy sources that can be used to meet energy demands, but most of the hydropower plants suffer from silt erosion and cavitation problems. Therefore, it is important to decide which parts to be repair or replace, as it affects the Operation and Maintenance (O&M) costs. Presently O&M costs are speculated based on the previous years O&M costs data. Various studies are available for forecasting O&M costs, but they are plant specific. This paper deals with the assessment of the O&M costs of hydropower plants, considering the parameters and conditions involved in the O&M costs of hydropower plants. The correlations are developed to predict the O&M costs of hydropower plants and found that the developed correlation models can predict the O&M costs with an accuracy having an R2-value of 0.89, Mean Absolute Percentage Error (MAPE) of 3.53% and Root Mean Square Percentage Error (RMSPE) of 4.45% for Francis turbine-based hydropower plants, and with an R2-value of 0.97, having a MAPE of 0.17% at 1.30% RMSPE for Kaplan turbine-based hydropower plants. This study may be useful for developers, plant operators, and researchers.

Repair and maintenance cost analysis of John Deere 5403 tractor in the Gambia

The paper presents an approach for deriving a mathematical model that predict repair and maintenance (R&M) cost of farm tractors in The Gambia. As John Deere (JD) tractors are widely used by Gambian farmers, a study was conducted to predict accumulated repair & maintenance costs (Y) of the two-wheel drive (2WD) JD-5403 tractor based on accumulated working hours (X). In order to determine the mathematical model for the studied tractor, regression analysis using knowledge based analytical software (SPSS STATISTICS 21 and Excel 2016 version) was performed on the calculated data generating five regression models: linear, logarithmic, polynomial, power and exponential. The statistical results showed that the polynomial model gave better cost prediction with higher confidence and less variation than other models. Finally, it was established that repair and maintenance cost increased with an increase in working hours of JD-5403 tractor.

Power Supply Based on Inductive Harvesting From Structural Currents

Monitoring infrastructure offers functional optimization, lower maintenance cost, security, stability, and data analysis benefits. Sensor nodes require some level of energy autonomy for reliable and cost-effective operation, and energy-harvesting methods have been developed in the last two decades for this purpose. Here, a power supply that collects, stores, and delivers regulated power from the stray magnetic field of current-carrying structures is presented. In cm-scale structures the skin effect concentrates current at edges at frequencies even below 1 kHz. A coil-core inductive transducer is designed. A flux-funneling soft magnetic core shape is used, multiplying power density by the square of funneling ratio. A power management circuit combining reactance cancelation, voltage doubling, rectification, supercapacitor storage, and switched inductor voltage boosting and regulation is introduced. The power supply is characterized in house and on a full-size industrial setup, demonstrating a power reception density of 0.36, 0.54, and 0.73 mW/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> from a 25-A root-mean-square structural current at 360, 500, and 800 Hz, respectively, corresponding to the frequency range of aircraft currents. The regulated output is tested under various loads and cold starting is demonstrated. The introduced method may enable power autonomy to wireless sensors deployed in current-carrying infrastructure.

Analysis and modelling of social housing repair and maintenance costs: A UK case study

Effective use of resources for maintaining social housing has long been a common goal of public bodies across the world. However, maintenance cost data is quintessentially sensitive and thus difficult to obtain, rendering the dearth of empirical maintenance cost studies on social housing. This research investigates the complexities of repair and maintenance (R&M) associated with social housing and specifically, develops benchmark indicators for such works. The ambition being to provide a knowledge sharing analysis of costs expended that allows a social housing provider to learn from past works undertaken with a view to optimising future practice. A mixed philosophical approach is adopted that combines elements of both pragmatism and interpretivism. A case study and participant action researcher (PAR) strategy is adopted where the lead researcher is employed within a repairs and maintenance (R&M) department of a UK Housing Association. Longitudinal quantitative R&M cost data is analysed using summary statistical, regression analysis and performance statistics (to measure predictive accuracy). Focus groups are held with housing practitioners and the cross-sectional qualitative discourse is analysed using content analysis to explain emergent patterns and trends accrued form the quantitative analysis conducted. This research identified that R&M works for a UK Housing Association follow a non-parametric distribution that is heavily positively skewed. Housing Associations without sufficient planned investment will see more sporadic distributions leading to less cost certainty. Furthermore, linear regression analysis provides an accurate fit of the cumulative R&M spend with very little deviation between actual and predicted R&M costs; hence, accurate forecasting is possible for Housing Associations. Finally, the sub-categorisation of works packages has indicated that certain work packages expend greater funds (often considered as being outlier costs) than others but linear regression models did not fit all sub-categorisations accurately. This research presents a unique insight into R&M costs incurred on social housing by a UK Housing Association to provide a vignette of contemporary practice and costs incurred. The work proves useful to housing associations, contractors and policy makers who seek to optimally balance cost and service delivered for residents.

Modeling Priority Maintenance Cost Based on Top-Down and Bottom-Up Approach with Reconciliation of Forum Group Discussion

PT XYZ engaged in the electricity industry, which has a strategic issue that’s reduction of BPP (Cost of Providing) electricity as one step in supporting the holding company policy. With the target of reduction BPP, where one component of the BPP is maintenance costs. This research shows the priority of maintenance costs of each Units that can be used in the drafting of the RKAP. In order to prioritize maintenance costs, it is necessary to compile an analysis of the cost, start from the proposed maintenance costs of each unit (using the bottom-up method) until evaluating cost from management (using the top-down method). This top-down method is strengthened by the analysis of priority maintenance costs from the expert's view using the Analytical Hierarchy Process (AHP) method with the Expert Choice V-11 Software tool. Then the results of prioritizing maintenance costs from the bottom-up and top-down methods are carried out reconciliation through Forum Group Discussion (FGD). Based on the results, that the highest weighting factor was Productivity (0.616), followed by Area (0.320), Specifications (0.064). Whereas the sub factor that is very dominant globally is having low production costs and the highest priority Unit for maintenance costs is Unit E.

Research on Civil Aircraft Direct Maintenance Cost Analysis and Optimization Based on Data Mining

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.

D-distance Risk Factor for Transmission Line Maintenance Management and Cost Analysis

In this paper, a D-distance risk factor was proposed to prioritize high-voltage transmission lines from high to low risk in transmission line maintenance and renovation management. Various conditions and importance assessment criteria together with the weighting and scoring method were proposed to calculate both the renovation and importance indices of transmission lines. The scores of different test methods and visual inspection were differentiated from zero to five as end-of-life to very good condition to evaluate the condition of the line and its components. Additionally, the scores of different importance criteria were modified to assess the line importance from low to high importance. Moreover, the analytic hierarchy process was applied to determine the important weight of all test methods and importance criteria, which were evaluated by utility experts. The renovation and importance indices were combined in a risk matrix to finally determine the risk of the line by using the D-distance technique. Later, the risk of every transmission line was plotted in a risk matrix to prioritize and manage maintenance tasks. Finally, a maintenance cost was analyzed by applying the D-distance risk factor and compared with the replacement cost of a new transmission line for maintenance planning and cost minimization. Twenty out of 115, 230 and 500 kV transmission lines fleet in Thailand were practically analyzed with actual data. The results were realistic to feasibly implement in a transmission system for sustainable management.

Techno-Economic Analysis and Planning for the Development of Large Scale Offshore Wind Farm in India

Despite India's great potential for offshore wind energy development, no offshore wind farm exists in the country. This study aims to plan a large scale offshore wind farm in the south coastal region of India. Seven potential sites were selected for the wind resource assessment study to choose the most suitable site for offshore wind farm development. An optimally matched wind turbine was also selected for each site using the respective power curves and wind speed characteristics. Weibull shape and scale parameters were estimated using WAsP, openwind, maximum likelihood (MLH), and least square regression (LSR) algorithms. The maximum energy-carrying wind speed and the most frequent wind speed were determined using these algorithmic methods. The correlation coefficient (R2) indicated the efficiency of these methods and showed that all four methods represented wind data at all sites accurately; however, openwind was slightly better than MLH, followed by LSR and WAsP methods. The coastal site, Zone-B with RE power 6.2 M152 wind turbine, was found to be the most suitable site for developing an offshore wind farm. Furthermore, the financial analysis that included preventive maintenance cost and carbon emission analysis was also done. Results show that it is feasible to develop a 430 MW wind farm in the region, zone B, by installing seventy RE power 6.2 M152 offshore wind turbines. The proposed wind farm would provide a unit price of Rs. 6.84 per kWh with a payback period of 5.9 years and, therefore, would be substantially profitable.