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@article{188461,
        author = {KHUSHABU G. GORLE and PRABHAKAR D. KHANDAIT and RUTUJA S. MALWE and ABHISHEK Y. SAHARE and PRANAY K. ZILPE},
        title = {Predictive Maintenance for Industrial Machines – A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2243-2248},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188461},
        abstract = {Industrial machines play a major role in modern manufacturing because they allow industries to produce goods faster and in large quantities. As factories expand and more equipment becomes automated or computer-controlled, keeping these machines healthy and reliable becomes extremely important. When any machine unexpectedly stops working, it can slow down the entire production line, harm other equipment, and even create safety risks for workers. This makes traditional maintenance methods less effective today, as they usually detect problems only after a breakdown or during routine inspections. Small faults often go unnoticed and gradually grow into serious issues, leading to costly downtime and operational difficulties. Predictive maintenance offers a better solution by identifying potential failures before they occur. It uses continuous monitoring and intelligent technologies to observe machine conditions in real time. Modern tools such as IoT sensors, cyber-physical systems, and industrial data analytics allow tracking of factors like vibration, temperature, pressure, and electrical current. The collected data is then processed using machine learning algorithms, which help detect unusual patterns that may signal early faults. Techniques like Random Forest, Support Vector Machines, and deep learning models are highly effective in identifying abnormal behavior in industrial systems.Studies show that combining IoT-based sensing with data-driven models creates reliable and scalable solutions for machine health monitoring. These advanced methods help extend machine life, reduce maintenance expenses, and improve overall production efficiency. With Industry 4.0, smart factories rely on connected devices, automated inspections, and intelligent monitoring to minimize downtime and ensure smooth, uninterrupted operations. Predictive maintenance has become essential for safer, smarter, and more efficient industrial environments.},
        keywords = {Failure Prediction, Fault Prediction, Machine Learning, Predictive maintenance, Systematic Review},
        month = {December},
        }