• Mahima Sahu
• Dr. Sandeep Garg

This Review paper presents a comprehensive review of recent research on predictive maintenance using IIoT and SCADA data. The study examines various data acquisition and preprocessing techniques, machine learning and deep learning approaches, system architectures, and implementation frameworks adopted in modern industrial environments. The rapid adoption of Industry 4.0 technologies has transformed traditional maintenance practices by enabling intelligent, data-driven decision-making for industrial equipment. Predictive Maintenance (PdM) has emerged as an effective approach for minimizing unexpected equipment failures, reducing maintenance costs, and improving operational efficiency. The integration of the Industrial Internet of Things (IIoT) and Supervisory Control and Data Acquisition (SCADA) systems provides continuous monitoring and real-time acquisition of operational data from industrial assets, creating a strong foundation for predictive analytics. This review paper presents a comprehensive analysis of data-driven predictive maintenance techniques that utilize IIoT and SCADA systems for fault detection, health monitoring, remaining useful life (RUL) estimation, and maintenance scheduling. It examines the role of machine learning, deep learning, and artificial intelligence algorithms in extracting meaningful insights from sensor and process data. The paper also reviews recent advancements in edge computing, cloud-based analytics, digital twins, and explainable AI that enhance the accuracy, scalability, and reliability of predictive maintenance solutions

"Review of Data-Driven Predictive Maintenance for Industrial Equipment Using IIoT and SCADA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.c169-c176, October-2026, Available :http://www.jetir.org/papers/JETIR2610222.pdf

"Review of Data-Driven Predictive Maintenance for Industrial Equipment Using IIoT and SCADA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppc169-c176, October-2026, Available at : http://www.jetir.org/papers/JETIR2610222.pdf