African Maintenance Engineering is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
PREDICTIVE MAINTENANCE OF POWER TRANSFORMER USING MACHINE LEARNING - A CASE STUDY
The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper resistance, distortions in winding caused by mechanical faults, failure of bushings, malfunction of tap changers, core malfunction, tank malfunction, failure of the protection system, and failure of the cooling system. Traditional methods for transformer fault detection involve using the ratio of key gases present in the transformer oil when a fault occurs. These gases include Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Ethyne (C2H2), Carbon Monoxide (CO) and Carbon Dioxide (CO2). For accurate and early detection of faults, traditional methods require complex algorithms. This project focuses on the predictive maintenance of power transformers using machine learning techniques, aiming to identify and address potential faults pre-emptively. By analysing various fault types and leveraging machine learning tech like Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbour (KNN), the project develops models that predict transformer failures based on historical data. Dataspell software and Python libraries such as Numpy and Matplotlib were used to train the model. The testing results showed the efficiency of the SVM, KNN, and Decision Tree methods in detecting the faults experienced by the power transformer. The testing accuracy for SVM, KNN and Decision Tree models was 95.65%, 95.65% and 89.13%, respectively. It was observed that the SVM and KNN models performed better than the decision tree model.
Copyright (c) 2026 African Maintenance Engineering
The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper resistance, distortions in winding caused by mechanical faults, failure of bushings, malfunction of tap changers, core malfunction, tank malfunction, failure of the protection system, and failure of the cooling system. Traditional methods for transformer fault detection involve using the ratio of key gases present in the transformer oil when a fault occurs. These gases include Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Ethyne (C 2H2), Carbon Monoxide (CO) and Carbon Dioxide (CO2). For accurate and early detection of faults, traditional methods require complex algorithms. This project focuses on the predictive maintenance of power transformers using machine learning techniques, aiming to identify and address potential faults pre-emptively. By analysing various fault types and leveraging machine learning tech like Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbour (KNN), the project develops models that predict transformer failures based on historical data. Dataspell software and Python libraries such as Numpy and Matplotlib were used to train the model. The testing results showed the efficiency of the SVM, KNN, and Decision Tree methods in detecting the faults experienced by the power transformer. The testing accuracy for SVM, KNN and Decision Tree models was 95.65%, 95.65% and 89.13%, respectively. It was observed that the SVM and KNN models performed better than the decision tree model.
Slope Stability Analysis of Road Embankments in the Mountainous Terrain of Eastern Equatoria, South Sudan
Road embankment failures in the mountainous terrain of Eastern Equatoria, South Sudan, represent a persistent threat to regional connectivity, humanitarian access, and economic activity in one of sub-Saharan Africa's most geotechnically challenging environments. The region's steep escarpment terrain, deeply weathered tropical soils, intense monsoon rainfall (1,200–1,800 mm/year), and near-total absence of site investigation data have historically resulted in embankment designs based on empirical rules-of-thumb with no formal factor of safety analysis. This study presents the first systematic slope stability assessment of road embankments along five critical routes in Eastern Equatoria (Torit–Kapoeta, Nimule–Juba, Kapoeta–Narus, Chukudum–Budi, and Lafon–Moli corridors), encompassing 28 embankment cross-sections identified as unstable or potentially unstable through field reconnaissance. Geotechnical characterisation included Standard Penetration Tests (SPT, n = 86 boreholes), laboratory triaxial and direct shear tests, and soil classification per USCS. Slope stability was analysed using the Bishop Simplified Method and Spencer's Method within the Slide2 software package, for both dry-season and post-rainfall saturation conditions. Results indicate that 61% of assessed embankments have Factors of Safety (FS) below the critical threshold of 1.25 under post-rainfall conditions, with five sections recording FS < 1.0 (indicating active or imminent failure). A rainfall-triggered pore water pressure model calibrated to local piezometer data reveals that embankment FS drops by 28–45% within 48 hours of a 100 mm/day rainfall event. Stabilisation recommendations including geosynthetic reinforcement, drainage improvements, slope regrading, and soil nailing are evaluated through par
Copyright (c) 2025 AJE-GeotechE https://creativecommons.org/licenses/by/4.0/
Digital Twin Framework for Monitoring Critical Road Infrastructure Across the East African Community
Digital twin technology — the creation and continuous synchronisation of a virtual replica of a physical asset using real-time sensor data, computational models, and machine learning algorithms — represents the most significant paradigm shift in infrastructure asset management since the introduction of pavement management systems in the 1980s. This paper presents the design, implementation, and performance evaluation of the EAC-DT, a scalable Digital Twin Framework for monitoring critical road and bridge infrastructure across the East African Community highway network. The EAC-DT integrates five data acquisition modalities — IoT sensor networks (accelerometers, strain gauges, acoustic emission sensors, piezometers), unmanned aerial vehicle (UAV) photogrammetry, satellite interferometric synthetic aperture radar (InSAR), weigh-in-motion (WIM) stations, and mobile road condition survey vehicles — into a unified cloud-based data lake, feeding a core digital twin engine that combines finite element model updating, BIM-GIS integration, and ensemble machine learning for real-time condition assessment and predictive maintenance. The framework was piloted on a 180 km segment of the Kenya A104 corridor (Nairobi–Mombasa) encompassing 14 bridges and 12 pavement management sections, and subsequently validated on the Uganda A109 (Kampala–Malaba, 130 km) and Tanzania T1 (Dar es Salaam–Morogoro, 95 km) corridors. Across the three pilot corridors, the EAC-DT achieved IRI prediction accuracy of RMSE = 0.21 m/km (compared to 0.68 m/km for conventional visual surveys), bridge structural health index prediction R² = 0.94, and an average 2.4-day advance warning of pavement distress events with 87% detection rate. Life-cycle cost analysis demonstrates that digital twin-enabled asset manageme
Copyright (c) 2023 AJOAIAUS https://creativecommons.org/licenses/by/4.0/
Comparative Performance of Asphalt Pavement on East African Community Highway Corridors
Asphalt pavement performance across the East African Community (EAC) highway network is critically compromised by a combination of high ambient temperatures, axle overloading, seasonally high rainfall, and maintenance underfunding. This study presents a multi-corridor comparative analysis of asphalt pavement performance on five major EAC highway routes — the Kenyan A104 (Nairobi–Mombasa), Tanzanian T1 (Dar es Salaam–Dodoma), Ugandan A109 (Kampala–Malaba), Rwandan RN1 (Kigali–Gatuna), and Ethiopian A1 (Addis Ababa–Djibouti) — covering a combined network length of 3,847 km. Performance indicators evaluated include International Roughness Index (IRI), rutting depth, cracking index, skid resistance (SFC), and structural number (SN). Field data were collected through Network-Level Pavement Condition Surveys (NLPCS) augmented by falling weight deflectometer (FWD) testing at 200 m intervals and core extraction for mix characterisation. The AASHTO mechanistic-empirical pavement design model and the World Bank HDM-4 deterioration framework were calibrated to EAC climatic and traffic conditions to predict 10-year performance trajectories. Life-cycle cost analysis (LCCA) was performed for four pavement strategy alternatives: conventional hot-mix asphalt (HMA), stone mastic asphalt (SMA), warm-mix asphalt (WMA), and WMA incorporating 30% reclaimed asphalt pavement (RAP). Results show that the Tanzania T1 corridor exhibits the most severe deterioration rate (IRI increase of 0.28 m/km/year), attributable to high axle overloading and weak subgrade CBR values (mean 4.8%). The WMA + 30% RAP strategy produces the lowest 30-year life-cycle cost at USD 1.94 million per km, representing a 28% saving over conventional HMA, while maintaining equivalent or superior structural performance. Reco
Copyright (c) 2026 AJE-MaintenanceE https://creativecommons.org/licenses/by/4.0/
The East African Community (EAC) highway network constitutes the arterial spine of regional economic integration, connecting landlocked member states to port gateways and enabling intra-regional trade, humanitarian logistics, and population mobility across eight member states spanning diverse climatic and geotechnical environments. This study presents a systematic comparative performance assessment of hot-mix asphalt pavement across six EAC highway corridors, integrating pavement condition surveys, structural deflection testing, climate exposure analysis, and lifecycle cost modelling. Pavement performance is evaluated through three primary indices: International Roughness Index (IRI), Pavement Condition Index (PCI), and mean rut depth, collected through standardised road condition surveys in 2024 across a combined corridor length of 4,840 km. Results reveal substantial performance disparities across the EAC network, with IRI values ranging from 2.8 m/km on the LAPSSET Corridor (Kenya–Ethiopia, semi-arid, high maintenance investment) to 6.7 m/km on the Kampala–Juba Corridor (Uganda–South Sudan, tropical with conflict-induced maintenance deficit). PCI scores range from 34 (Kampala–Juba, poor condition) to 79 (LAPSSET, good condition). Pavement distress type analysis identifies fatigue cracking (22–31%) and rutting (15–35%) as the dominant failure mechanisms across all corridors, with the relative proportion of potholing strongly correlated with annual rainfall intensity and maintenance budget per kilometre. Exponential PCI deterioration models are calibrated for each corridor, yielding deterioration rate coefficients β of 0.028–0.078 per year, with the Kampala–Juba corridor exhibiting deterioration rates 2.8 times higher than the best-performing LAPSSET corridor. A multi-
Copyright (c) 2024 AJE-MaintenanceE https://creativecommons.org/licenses/by/4.0/
Process-Control Systems Evaluation in Senegal
In Senegal, process-control systems have been implemented in various industries to enhance operational efficiency and yield. A DiD analysis was applied to compare pre- and post-intervention yields in control and intervention groups. Uncertainty around estimates is reported with robust standard errors. The DiD model revealed a significant increase of approximately 15% in yield for the intervention group compared to controls, but this effect varied by sector. Process-control systems have shown promise in improving yields across different industries in Senegal, though effects differ significantly between sectors. Further research should focus on identifying and addressing sector-specific challenges to maximise system effectiveness. DiD model, process control systems, yield improvement, Senegal The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis for Evaluating Water Treatment Facility Systems in Tanzania
Water treatment facilities in Tanzania face challenges related to system reliability due to varying economic conditions and operational environments. A multilevel regression analysis will be employed to model the influence of economic conditions (at the district level) and operational environments (at the facility level) on water treatment system reliability in Tanzania. The study will use a dataset comprising over 50 facilities across 10 districts, with data collected through surveys and official records. A significant proportion (34%) of surveyed facilities reported maintenance issues related to financial constraints at district levels, highlighting the need for integrated economic support strategies. The multilevel regression analysis confirmed that both economic conditions and operational environments significantly impact water treatment system reliability in Tanzania. The study provides a novel methodological framework for evaluating such systems. Policy makers should prioritise financial assistance to districts with lower maintenance levels, while also improving monitoring and maintenance protocols at facility level to enhance overall system performance. water treatment facilities, multilevel regression analysis, reliability, economic conditions, operational environments The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Climate-Resilient Design for Urban Drainage Systems in Coastal Ghana
Urban drainage systems in coastal areas of Ghana are vulnerable to climate-induced flooding due to rising sea levels and increased intensity of rainfall events. A hybrid modelling approach combining hydrological simulations with socio-economic data was employed to assess flood risk and identify optimal infrastructure solutions. The modelled results indicate an average reduction of 20% in flood frequency within the study area under future climate scenarios, with a 95% confidence interval for this estimate. The designed drainage system is expected to enhance resilience against flooding by managing runoff more effectively and reducing peak flow rates during heavy rainfall events. Implementation of the proposed design should be prioritised through collaboration between urban planners, engineers, and local communities. Climate resilience, Urban drainage systems, Coastal Ghana, Flood risk management The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model for Measuring Adoption Rates of Process-Control Systems in Kenya
The adoption of process-control systems in Kenyan manufacturing facilities has been observed to vary over time, influenced by factors such as technological advancements and economic conditions. A time-series forecasting model was employed, incorporating autoregressive integrated moving average (ARIMA) methodology to analyse historical data from selected Kenyan manufacturers. Robust standard errors were used for uncertainty quantification. The ARIMA model predicted a steady increase in adoption rates over the next five years with an estimated growth rate of 12% annually, based on observed trends and external factors like government incentives and technological improvements. The time-series forecasting model demonstrated its effectiveness in predicting future adoption rates, providing valuable insights for manufacturers and policymakers in Kenya's manufacturing sector. Manufacturers should consider the ARIMA forecast to plan their investment strategies. Policymakers could leverage these findings to design targeted interventions that encourage greater adoption of process-control systems. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Bayesian Hierarchical Model for Measuring System Reliability in Municipal Infrastructure Assets Systems of Tanzania
The reliability of municipal infrastructure assets in Tanzania is crucial for sustainable urban development. A Bayesian hierarchical model was applied to municipal infrastructure data, accounting for spatial variability and heterogeneity across regions. The analysis revealed significant differences in system reliability between urban and rural areas, with an estimated mean reliability of 85% (95% credible interval: 70-92%). The developed model provides a robust framework for assessing municipal infrastructure systems' performance. Policy makers should prioritise maintenance in regions with lower system reliability to improve overall urban resilience. Bayesian Hierarchical Model, Municipal Infrastructure, System Reliability, Tanzania The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis of Adoption Rates in Transport Maintenance Depots Systems in Tanzania
This study addresses a current research gap in Engineering concerning Methodological evaluation of transport maintenance depots systems in Tanzania: multilevel regression analysis for measuring adoption rates in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of transport maintenance depots systems in Tanzania: multilevel regression analysis for measuring adoption rates, Tanzania, Africa, Engineering, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Time-Series Forecasting Model Evaluation for Yield Improvement in Water Treatment Facilities in Ghana, 2009
Water treatment facilities in Ghana have experienced varying yields over time due to fluctuations in water supply and operational efficiency. The study employs an autoregressive integrated moving average (ARIMA) model to forecast future yields based on historical data from five water treatment plants in Ghana. Robust standard errors are used for inference. A significant trend of yield improvement was observed, with a predicted increase of approximately 10% over the next three years due to optimised maintenance schedules and operational adjustments. The ARIMA model accurately forecasts yield improvements in Ghanaian water treatment facilities, suggesting that targeted maintenance interventions can enhance efficiency. Implementing regular maintenance checks and optimising operation protocols could lead to substantial yield benefits for the evaluated plants and potentially other facilities. Water Treatment Facilities, Yield Improvement, Time-Series Forecasting, ARIMA Model The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Water Treatment Facilities in Kenya Using Panel Data for Cost-Effectiveness Analysis
This study addresses a current research gap in Engineering concerning Methodological evaluation of water treatment facilities systems in Kenya: panel-data estimation for measuring cost-effectiveness in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of water treatment facilities systems in Kenya: panel-data estimation for measuring cost-effectiveness, Kenya, Africa, Engineering, intervention study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Multilevel Regression Analysis for Evaluating Process-Control Systems in Ethiopian Maintenance Engineering Context
This study addresses a current research gap in Engineering concerning Methodological evaluation of process-control systems systems in Ethiopia: multilevel regression analysis for measuring cost-effectiveness in Ethiopia. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of process-control systems systems in Ethiopia: multilevel regression analysis for measuring cost-effectiveness, Ethiopia, Africa, Engineering, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/