A Machine Learning Framework for Predictive Maintenance of Centrifugal Water Pumps using Remaining Useful Life Estimation

Abstract

Water pumps play a crucial role in various industrial, agricultural, and municipal applications. However, their efficiency and reliability are often compromised by unexpected failures, resulting in costly downtime. Traditional maintenance strategies like corrective and 
preventive maintenance have limitations in accurately predicting failures. This study explores the use of machine learning (ML) 
techniques, particularly Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM), to develop a predictive maintenance system that estimates the Remaining Useful Life (RUL) of centrifugal water pumps. MATLAB is used for data preprocessing, feature extraction, model training, and deployment. Sensor data is analyzed using statistical and frequency-domain 
techniques to extract health indicators, which are then used to train and validate predictive models. The results demonstrate the model's capability to accurately forecast pump failures, allowing timely interventions and reduced maintenance costs. This research highlights the potential of ML based predictive maintenance systems to enhance reliability and efficiency in pump operations.