Development of an Intelligent Predictive Maintenance System Using Machine Learning for Industrial Equipment

Year 2025, Volume: 04, 31 - 42, 31.10.2025

Dıckson Davıd Olodu , Andrew Erameh , Osagie Imevbore Ihenyen , Francis Inegbedion

https://doi.org/10.54709/joebs.1662975

Abstract

The reliability and efficiency of industrial equipment are crucial for minimizing downtime and operational costs. This study presents the development of an intelligent predictive maintenance system using machine learning to enhance equipment reliability. Failure data from CNC machines, conveyor belts, lathe machines, boilers, and hydraulic presses were analyzed, revealing an annual downtime of 400 hours and maintenance costs of ₦20,000,000. Sensor data from IoT-enabled devices recorded vibration (2.5–7.0 mm/s), temperature (60–88°C), pressure (5.0–8.0 bar), and humidity (30–55%), with anomaly scores reaching 0.95. A machine learning framework tested Random Forest, SVM, Neural Networks, XGBoost, and Logistic Regression, with XGBoost achieving the highest accuracy (96.0%), precision (95.3%), recall (94.7%), and F1-score (95.0%). After implementing the predictive maintenance system, downtime was reduced by 45% (from 400 to 220 hours), maintenance costs decreased by 40% (from ₦20,000,000 to ₦12,000,000), and unexpected failures dropped by 66% (from 30 to 10 incidents annually). The mean time between failures increased from 300 to 500 hours (67% improvement), and spare parts usage was reduced by 30%. Feature importance analysis ranked vibration (0.35), temperature (0.30), and pressure (0.20) as key indicators of failure. A comparison of maintenance strategies showed predictive maintenance extended equipment lifespan to 12 years, outperforming reactive (8 years) and preventive (10 years) approaches. The developed system demonstrated significant improvements in reliability, cost savings, and operational efficiency, underscoring its potential for industrial adoption.

Keywords

Anomaly Detection, , Machine Learning, , Predictive Maintenance, , Sensor Data, , System Reliability

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