Predictive maintenance of centrifugal water pumps using machine learning: a case study of National Water and Sewerage Corporation

Abstract

This thesis explores Effective predictive maintenance strategies for Centrifugal water pumps, focusing on Uganda’s National Water and Sewerage Corporation (NWSC) and other similar large-scale water providers, aiming to improve water supply reliability for over 21 million people and reduce 185,000 annual customer complaints caused by 70% pump failures and 8-12 hours of operational downtime. However, despite advances in machine learning, tailored predictive maintenance approaches for water pumps in Uganda are understudied. Thus, this study developed a based predictive maintenance model for the centrifugal pumps using real-time operational data from National Water and Sewerage Corporation (NWSC) (N=13 pumps from the 3 pump stations i.e. (Gunhill, Katosi and uyenga)were analyzed. This study presents a comprehensive Machine-learning based predictive maintenance framework for estimating pump failure. The process integrates data preprocessing, extraction of statistical time-domain condition indicators, and evaluation of 5 machine learning algorithms; XGBoost, LightGBM, CatBoost, Random Forest, and a Voting Ensemble [applied to shift maintenance from a monthly health check to real-time monitoring] providing deeper insights into pump availability and health for future years. The primary objective was to accurately classify the pumps’ operational status into five distinct states: CHANGE, CRITICAL, OFF, OPERATIONAL, and WARNING. The results demonstrate that Extreme Gradient Boosting (XGBoost) model achieved superior predictive performance yielding an accuracy of 74% in detecting failure within pumps before more damage was done. Thus, leveraging of Machine Learning for Predictive maintenance enabled National Water and Sewerage Corporation to detect any anomalies in the Centrifugal pumps like; inconsistencies in flow rates, pressure fluctuations, vibration abnormalities etc. which helped reduce on the maintenance costs from (10-40%), reduce on equipment failure (70-75%), reduced on downtime (35% -45%) and lastly, increased on production capacity by(25%) thus improving on the well-being of the people in Uganda and promoting of SDG 6( Clean water and Sanitation). Keywords: Predictive Maintenance, Machine Learning, Centrifugal Pumps, National Water and Sewerage Corporation, Arduino, Classification Models