Energy Audit And Motor Failure Forecasting


EOI: 10.11242/viva-tech.01.08.048

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Citation

Prasad Mirgal, Prof. Bhushan Save, Pratik Gawade, Prathamesh Kirve, "Energy Audit And Motor Failure Forecasting", VIVA-IJRI Volume 1, Issue 8, Article 1, pp. 1-7, 2025. Published by Electrical Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

“Energy Audit And Motor Failure Forecasting” Industrial facilities often face energy inefficiencies due to outdated equipment and poor maintenance practices. These inefficiencies can result in significant operational costs and compliance issues. This paper explores the integration of energy audits with motor failure forecasting to address such challenges. By analyzing energy consumption patterns and employing machine learning algorithms for predictive maintenance, organizations can achieve substantial cost reductions and operational reliability. The methodology involves the development of a microprocessor-based real-time data acquisition system and a mobile application for forecasting. The app, designed using Android Studio with Java or Kotlin, consolidates historical and real-time data, providing actionable insights to maintenance teams. This integration significantly enhances efficiency, reduces downtime, and supports sustainability by minimizing the carbon footprint. Case studies demonstrating successful implementations highlight the benefits of combining these technologies. Ultimately, the study emphasizes the role of proactive energy management in driving industrial excellence.

Keywords

Carbon footprint, Energy audits, Industrial efficiency, Machine learning, Motor failure forecasting, Predictive maintenance, Sustainability

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