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@article{193405,
        author = {Pranita K. Gaurkar and Prof. Sonali A. Sabnis},
        title = {IOT-BASED PREDICTIVE MAINTENANCE FOR ELECTRICAL MACHINES AND INDUSTRIAL AUTOMATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {264-268},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193405},
        abstract = {The rapid adoption of Industry 4.0 has accelerated the integration of the Internet of Things (IoT), machine learning (ML), and data analytics for predictive maintenance (PdM) of industrial assets. Traditional maintenance strategies such as reactive and preventive maintenance often lead to unplanned downtime, excessive costs, and inefficient resource utilization. IoT-based predictive maintenance leverages real- time sensor data, cloud/edge computing, and intelligent algorithms to predict equipment failures before they occur. This literature survey reviews recent advancements in IoT-driven predictive maintenance systems from 2020 to 2024, focusing on system architectures, sensing modalities, data analytics techniques, and application domains. A comparative analysis highlights the strengths, limitations, and research gaps of existing approaches, emphasizing future directions toward scalable, real-time, and energy- efficient PdM solutions.},
        keywords = {Internet of Things (IoT), Predictive Maintenance, Industry 4.0, Machine Learning, Condition Monitoring, Smart Manufacturing, Fault Detection, Industrial Equipment, Data Analytics.},
        month = {March},
        }