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Predictive Maintenance Using Machine Learning

Year: 2026

Type: article

Abstract: Predictive Maintenance Using Machine Learning is a transformative approach designed to address the inefficiencies of traditional industrial maintenance strategies. Unlike reactive maintenance (fixing after failure) or preventive maintenance (scheduled regardless of condition), this project leverages machine learning models to analyze historical and real-time data to identify potential equipment failures before they occur. The system employs algorithms such as Random Forest and Neural Networks to predict equipment conditions and generate real-time alerts. By integrating IoT sensor data (vibration, temperature, pressure), the application enables industries to reduce unplanned downtime, lower maintenance costs, and extend equipment lifespan. The project demonstrates a scalable solution applicable across industries, aligning with Industry 4.0 standards for operational efficiency and reliability.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19633816

Authors: Atharv Mandawkar, Arpit Bante, Shweta Rahangdale

Institution: Rashtrasant Tukadoji Maharaj Nagpur University

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7154698343

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