Effect of Hours of Use and Age in Years in Estimating Repair and Maintenance Costs for Two Sizes of Agricultural Tractors in Northern Sudan

Repair and maintenance cost is considered as one of important items for machinery management and selection especially agricultural tractors. The present study was carried out in Dongola area for tractor repair and maintenance costs estimation. The data was collected from records of Elshimalya Company for Agricultural services. Forty-four tractors representing two powers sizes, 75hp and 150hp used in the area were selected for this study. Based on the data collected, regression correlation analysis was carried out and mathematical models were derived to predict the accumulated repair and maintenance (R and M) costs as percent of purchase price in relation to accumulated hours of use and age (years) for each tractor size, and for the two sizes collectively. Five model forms (linear, logarithmic, polynomial, power and exponential) were derived and the power function was found the best fit to explain the relation. The accumulated Rand M costs as percent of purchase price (Y) was increased as the accumulated hours of use (x) and age (g) of the tractor in years were increased. A high correlation was found between the accumulated R and M cost and both accumulated hours of use and tractor age in years (Average R2 = 0.93). It was concluded that the power function was the best fit for repair and maintenance cost estimations and this relation may be used as an average of the two tractor powers, for estimation of the accumulated R and M costs as percent of purchase price (Y) with accumulated hours of use (x) and age (g): Y=0.028x0.662 (mean) Y=12.294g1.276 (mean).

Repair and maintenance cost is considered as one of the most important items for machinery reliability and management especially agricultural tractors. The current study was conducted in Dongola area, Sudan for the purpose of estimating tractor repair and maintenance expenses. The repair and maintenance (R&M) cost is an important item in the costs of ownership and operation. The information was gathered from Elshimalya Company for Agricultural Services records. In the study region, 44 tractors with two different power sizes—75 horsepower and 150 horsepower—were chosen. Regression correlation analysis was used to forecast the cumulative repair and maintenance (R and M) costs as a percentage of purchase price in connection to the cumulative hours of usage and age (years) for each tractor size as well as for the two sizes taken together based on the data gathered. Five model forms were developed (linear, logarithmic, polynomial, power, and exponential), and the power function was determined to be the best fit to explain the relationship. The accumulated Rand M costs as a percentage of the purchase price (Y) increased as the tractor's accumulated hours of use (x) and age (g) in years climbed. The accumulated R&M cost was shown to have a significant association with both accumulated hours of use and tractor age in years (Average R2 = 0.93- 0.95).It was determined that the power function was the best fit for repair and maintenance cost estimations and this relation may be used as an average of the two tractor powers, for estimation of the accumulated R and M costs as percent of purchase price (Y) with accumulated hours of use (x) and age (g) as follows: Y=0.028x0.662 (mean), Y=12.294g1.276 (mean).

PurposeThe purpose of this paper is to investigate tractor maintenance practices and costs in Nigeria with a view to making replacement decisions and for overall farm budgeting.Design/methodology/approachThe information was obtained via a structured questionnaire. Tractor owners were grouped into three categories: government agricultural settlements, established farms, and individual farms. Data used for analysis were collected from 60 farms located in major farming centers of the southwestern part of Nigeria. The report includes the formulation of repair equations, which relate tractor repair cost as a percentage of initial purchase prices to cumulative hours of use. Life cycle costs of three commonly‐used tractors were also estimated. The derived equations, which showed that the repair costs per hour increased with hours of use, were used to obtain the life cycle cost of each tractor model in different tractor owners' farms.FindingsThe proposed equation was found to be the best fit with coefficient of determination (R2) ranging from 0.992 to 0.996 for tractors in the three user categories. The trend observed in the life cycle costing of the three common tractors gave the general picture of when to replace each of the tractors based on the strategies being adopted by the tractor owners.Practical implicationsThe results of this study would serve as baseline information to tractor owners regarding optimum use of tractors for minimizing repair and maintenance cost per hour and for making replacement policy. It would also be helpful to government and other agencies for future planning in the provision of tractor services to the farmers at relatively lower repair and maintenance cost.Originality/valueProves that a reduction in repair costs by careful operation and adequate maintenance could result in a significant reduction in tractor ownership costs.

Estimation of tractor repair and maintenance costs

Frequent damage to the grinding disc components and the transmission system of the 15 kg/hour rice flour grinder is a problem faced. The purpose of scheduling component maintenance and repairs is to obtain predictions of maintenance and repair schedules and costs for the period 2026. The component maintenance and repair scheduling method includes examining previous period maintenance and repair data, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, estimating component life, estimating technician costs, estimating supporting work equipment and supporting materials to be used in maintenance, estimating the time for replacing spare parts or reinstalling components after repair, estimating maintenance and repair costs in 2026, and calculating the maintenance cost to profit ratio. The results of component maintenance and repair scheduling show that the maintenance cost in 2026 is Rp 1,370,000,- with an estimated annual profit potential of Rp 21,600,000, and the maintenance cost to profit ratio is 6.3%, which implies that the 15 kg/hour rice flour grinder is still quite prospective and feasible to use for the next few years.

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.

The problems encountered are damage to the rubber wheel mount and universal/cross joints on the 90 m/hour capacity wood profile making machine, which can affect the uniformity and speed of wood profile making. Maintenance and repair planning aims to be able to create a maintenance and repair schedule for the 90 m/hour capacity wood profile making machine for the period 2026, estimate maintenance costs and the ratio of maintenance and repair costs to machine profits. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing the specifications of the wood profile making machine, estimating the age and price of components that are estimated to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement spare parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the period 2026, and determining the ratio of maintenance costs to profits. The planning results in the form of a maintenance-repair schedule for the period 2026; maintenance and repair costs in 2026, the ratio of maintenance costs to profits, and their implications indicate that the machine is still prospective and usable.

Many tractor makes of two-wheel drive (2WD) types are working in the Gezira irrigated agricultural scheme, Sudan. Private individual farmers own most of them. However, information regarding their repair and maintenance costs are insufficient. Moreover, depending on information on repair and maintenance costs from other farming systems or countries is unrealistic. The objectives of this study were to develop power mathematical model to predict repair and maintenance costs for tractors make and their general model for tractors owned by individual private farmers in the Gezira irrigated scheme. The required data was obtained through structured questionnaire. The collected data included tractor make, model and initial purchase price, in addition, to tractor’s annual repair and maintenance expenses. Moreover, the annual working hours for tractors were calculated. Five tractors make were studied; namely Massey Ferguson (MF 290), New Holland (TT 75), Hattat 290, Tafe 8502 and John Deere (JD 5075E). Power models were developed for these tractors make and their general model. The results showed that there was strong and highly significant correlation between annual repair and maintenance cost as percentage of purchase price (y) and annual hours of use (x). The coefficients of determination (R2) were between 0.85 and 0.99 and the significance levels (P) were (P < 0.01 to 0.001) for all tractors make and the general model. The established power models were (y = 0.0007 x 0.7268), (y = 0.0026 x 0.5491), (y = 0.0009 x 0.7207), (y = 0.0042 x 0.4899), (y = 0.0001 x1.0136) and (y = 0.0011 x 0.6827) for MF 290, TT 75, Hattat 290, Tafe 8502, JD 5075E tractors make and their general model, respectively. The general model predicted lower values than those models in the Sudan and other countries. The results revealed that tractor purchase price and annual hours of use influenced repair and maintenance costs, but purchase price had great effect compared to hours of use. Models for predicting tractor repair and maintenance costs should be updated periodically to diminish the influence of the involved factors in calculations such as prices and usage as well as other factors like services received.

The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership.

The paper presents an approach for deriving repair and maintenance factors intended to indicate the accumulated repair and maintenance costs for agricultural machines. In a two-stage approach, an annual ‘repair and maintenance cost’ function is estimated and afterwards aggregated for the machine’s estimated service life. Based on cross-sectional data, the approach is applied for tractors, ploughs, mowers and self-loading trailers in Switzerland, covering a wide range of agricultural mechanisation. The results of our study show that, in line with the literature, an additional year in service increases annual repair and maintenance costs for all machine types under consideration. Furthermore, annual utilisation strongly influences repair and maintenance costs, a fact which, to our knowledge, has so far not been taken account of in the literature. For all analysed machines, increasing annual utilisation leads to a disproportionately low increase in repair and maintenance costs, revealing the existence of an economy-of-scale effect. Assuming that the machine’s estimated service life (also called estimated useful life) is completely exploited, the accumulated repair and maintenance costs depend strongly on the machine’s annual utilisation. Accordingly, in order to minimise accumulated repair and maintenance costs, high annual utilisation coupled with a short length of service life is beneficial.

This research proposes an artificial neural network (ANN)-based repair and maintenance (R&M) cost estimation model for agricultural machinery. The proposed ANN model can achieve high estimation accuracy with small data requirement. In the study, the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters. The model inputs are geographical regions, harvest area, and curve fitting coefficients related to historical cost data; and the ANN output is the estimated R&M cost. Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm. The R&M costs are estimated using the ANN-based model, and results are compared with those of conventional mathematical estimation model. The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%, indicating the proposed ANN model’s high predictive accuracy. The proposed ANN-based model is useful for setting the service rates of agricultural machinery, given the significance of R&M cost in profitability. The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy. Besides, the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility. Moreover, with minor modifications, the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin. Key words: repair and maintenance cost, estimation model, artificial neural network, curve fitting coefficients, combine harvesters DOI: 10.25165/j.ijabe.20231602.5931 Citation: Numsong A, Posom J, Chuan-Udom S. Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters. Int J Agric & Biol Eng, 2023; 16(2): 38－47.

It is well known that the pumps are the largest consumers of industrial motor energy and account for more than 25% of electricity consumption. The life cycle cost of a pump is the total lifetime cost associated with procurement, installation, operation, maintenance and its disposal. For majority of heavy usage pumps, the lifetime energy and/or maintenance cost will dominate the life cycle costs. Hence a greater understanding of all the cost components making up the total life cycle costs should provide an opportunity to achieve a substantial savings in energy and maintenance costs. This will further enable optimizing pumping system efficiency and improving pump and system reliability. Therefore in this context, the life cycle cost analysis of heavy usage pumps is quite important. This paper focuses on an application of a methodology of determining the life cycle cost of a typical heavy usage multistage centrifugal pump. In this case, all the cost components associated with the pump-set have been determined and classified under different categories. The data with regard to initial investment costs, operation costs, maintenance and repair costs and disposal costs for the pump considered for this case study was collected from the concerned pump manufacturer along with the unit cost of each component, quantity used and their weights. By applying the principles of reliability and maintainability engineering and using the data obtained from the design, manufacturing and maintenance departments, the component-wise values of MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) were estimated. The results of the life cycle cost analysis of the specimen pump were compared with the life cycle costs of similar pumps reported in the literature. From this comparison of results, it can be concluded that, the initial cost of the pump is the only a fraction of the total life cycle cost. The operating cost of the pump dominates the life cycle costs especially in case of heavy usage pumps. The maintenance cost varies approximately from 0.6 to 2.5 times the initial cost of the pump. The life cycle cost of the pump varies approximately from 12 to 33 times the initial cost of the pump. The operation and maintenance cost is almost 92 to 97 per cent of the life cycle cost. The detailed analysis carried out in this paper is expected to provide guidelines to the pump manufactures/practicing engineers in selecting a heavy usage multistage centrifugal pump based on the total lifetime cost rather than only on initial price.

Farm tractors breakdown classification

Educational facilities hold a higher degree of uncertainty in predicting maintenance and repair costs than other types of facilities. Moreover, achieving accurate and reliable maintenance and repair costs is essential, yet very little is known about a holistic approach to learning them by incorporating multi-contextual factors that affect maintenance and repair costs. This study fills this knowledge gap by modeling and validating deep neural networks to efficiently and accurately learn maintenance and repair costs, drawing on 1213 high-confidence data points. The developed model learns and generalizes claim payout records on the maintenance and repair costs from sets of facility asset information, geographic profiles, natural hazard records, and other causes of financial losses. The robustness of the developed model was tested and validated by measuring the root mean square error and mean absolute error values. This study attempted to propose an analytical modeling framework that can accurately learn various factors, significantly affecting the maintenance and repair costs of educational facilities. The proposed approach can contribute to the existing body of knowledge, serving as a reference for the facilities management of other functional types of facilities.

The maintenance cost depends on various factors such as building volume, floor area, number of household and so on. The maintenance cost of the apartment housing is affected by the maintenance type, building physical factor, sociogeographic aspects. Among these, the maintenance characteristics is represented and made up by the total floor area and number of household which means main factor to provide the building scale roughly. In this paper, it aimed at modelling the estimation function of the maintenance cost with the total floor area and number of household and analyzing the elasticity of the two factors. Although items of maintenance cost are various in general cost, repair cost and so on, we classified these items into the 5 categories. 5 categories are a general cost, a facility maintenance cost, a utilization cost, insurance and sanitary cost. The estimation function used a power function and it has better goodness-offitness than any other estimation methods in statistics. A power function has a three curve types with concave and convex and linear style to the origin.