The adoption of solar-powered water pumps is crucial for sustainable
water management, particularly in remote and agricultural areas.
However, the reliability of these systems is essential, as faults can result
in significant downtime and expensive repairs. This paper introduces
an intelligent fault detection model using logistic regression, a machine
learning technique suitable for binary classification of faults. It
leverages real-time sensor data, including electrical and mechanical
parameters, alongside environmental conditions, to predict potential
faults. The model was trained on a dataset from operational
installations, undergoing pre processing for feature enhancement and
missing data management. Evaluating the model involved key
performance metrics such as accuracy, precision, recall, and the Area
under the ROC curve, which confirmed its effectiveness in accurately
detecting faults with high precision. A comparative analysis with
decision trees and support vector machines emphasized logistic
regression’s efficiency. The research contributes to intelligent
monitoring systems and identifies future directions for enhancing fault
detection methods.
Yogesh Kakasaheb Shejwa, Jayraja U. Kidav, Lakshaman Korra, Manjiri Lavadkar
National Institute of Electronics and Information Technology, India
Solar Powered, Solar Water Pump, logistic Regression, Machine Learning, Intelligent Monitoring, Maintenance, Power Electronics
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 6 , Issue: 2 , Pages: 765 - 771 )
Date of Publication :
March 2025
Page Views :
12
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