
International Journal on Science and Technology
E-ISSN: 2229-7677
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Impact Factor: 9.88
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Volume 16 Issue 3
July-September 2025
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Development of a Condition-Based Maintenance (CBM) Model to Enhance Equipment Reliability in Water Treatment Plant: An Analysis of Tanga UWASA, Tanzania.
Author(s) | Eng. Joseph Samson Mkuki |
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Country | Tanzania |
Abstract | Tanga Urban Water Supply and Sanitation Authority (Tanga UWASA), Tanzania, is the focus of this study, which attempts to identify important factors, develop, and validate a Condition-Based Maintenance (CBM) model to improve equipment reliability in water treatment plants. It discusses the drawbacks of time-based and reactive maintenance systems and emphasizes how condition-based maintenance can improve efficiency, cut down on downtime, and save expenses. Twelve technical factors were assessed using real-time operational data and a structured survey; seven of these were determined to be crucial for the implementation of CBM by Relative Importance Index (RII) analysis. The effect of these factors on equipment reliability was then measured using a multiple regression model, which produced an R2 value of 0.910. After a year of validation, the model's predictive accuracy against real performance data was 97%. According to the results, maintenance plans in water treatment facilities with limited resources can be greatly enhanced by using efficient CBM models that incorporate vibration analysis, pressure monitoring, power quality, and other technical indicators. Through data-driven maintenance planning, this study offers engineers and policymakers in sub-Saharan Africa and around the world empirical support and useful recommendations for sustainable water infrastructure. |
Keywords | Relative Importance Index (RII), Water Treatment Plant, Condition-Based Maintenance (CBM), Equipment Reliability and Multiple Regression Model. |
Field | Engineering |
Published In | Volume 16, Issue 3, July-September 2025 |
Published On | 2025-09-04 |
DOI | https://doi.org/10.71097/IJSAT.v16.i3.8075 |
Short DOI | https://doi.org/g92nsw |
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IJSAT DOI prefix is
10.71097/IJSAT
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