Machine Learning-enabled Smart Sensors for Real-time Industrial Monitoring: Revolutionizing Predictive Analytics and Decision-making in Diverse Sector
Onyinye Obioha-Val
*
Computer and Electrical Engineering Department, University of District of Columbia, 4200 Connecticut Avenue NW Washington DC 20008, United States of America.
Oluwaseun Oladeji Olaniyi
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
Oluwatosin Selesi-Aina
University of Lagos, University Road Lagos Mainland Akoka, Yaba, Lagos, Nigeria.
Michael Olayinka Gbadebo
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
Titilayo Modupe Kolade
Ministry of Foreign Affairs, Tafawa Balewa House, Central Business District, Abuja, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This study investigates the integration of machine learning (ML) algorithms with smart sensor technologies across manufacturing, energy, and healthcare sectors, focusing on their impact on real-time industrial monitoring, predictive maintenance, and operational efficiency. By utilizing data from the UCI Machine Learning Repository and Kaggle, this research measures the effectiveness of ML-enabled sensors in reducing machine downtime and enhancing fault detection. Time series analysis and regression modeling reveal that sensor integration leads to a significant 5.5% improvement in machine uptime, raising average uptime from 91.5% to 97%, thus validating the role of predictive maintenance. Cost-benefit analysis further highlights that the energy sector achieves the highest financial returns, with a 33.3% ROI and a positive Net Present Value (NPV) over five years, demonstrating substantial cost savings relative to initial investment. Findings underscore the importance of sensor infrastructure compatibility, emphasizing the need for adaptable frameworks such as edge computing and digital twin technology to ensure efficient integration with legacy systems. Recommendations include industry-wide adoption strategies that leverage these technologies to optimize predictive maintenance and maximize sector-specific financial returns.
Keywords: Machine learning, smart sensors, predictive maintenance, operational efficiency, cost-benefit analysis