Skip to content
Research Article Open access CC BY 4.0

Repair and Maintenance Cost Estimation for Two Power Sizes of Agricultural Tractors as Affected by Hours of Use and Age in Years: A Case Study, Dongola Area, Sudan

Mohamed H. Dahab, Montasir A. Gafar, Abdul Gadir M. Abdul Rahman

Journal of Engineering Research and Reports · pp. 113–121 · Published 26 Jul 2021

10.9734/jerr/2021/v20i1017395

Abstract

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 mathematical model hours of use power Dongola

Cited by 5

Agriculture Vehicles Predictive Maintenance With Telemetry, Maintenance History and Geospatial Data

Lviv Polytechnic National University, Department of Computerized Automatic Systems, Anton Shykhmat, Zenovii Veres · Advances in Cyber-Physical Systems · 2024

Improving Agricultural Vehicle RUL Prediction: The Effectiveness of GIS Data in LSTM, RNN, and FCNN Models

Anton Shykhmat, Zenoviy Veres · 2025 IEEE 13th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · 2025

Analysis of the Structure of Agricultural Machinery Repair Workshops - A Case Study

Željko Barač, Tomislav Jurić, Ivan Plaščak · Lecture Notes in Networks and Systems · 2024

Test Results of Flushing Fluid “Diesel Purge”

T. Alushkin, A. Zubritskii, A. Ratkin · Lecture Notes in Mechanical Engineering · 2022

Geospatial and Wavelet-Based Feature Fusion for Advanced RUL Forecasting in Agricultural Machinery

Lviv Polytechnic National University, Anton Shykhmat, Volodymyr Solskyi · Advances in Cyber-Physical Systems · 2025

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

5

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.