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@article{168680,
        author = {Yuvraj Jivan Jadhav and Dr. Bhagyashala A. Jadhawar},
        title = {Real-Time Anomaly Detection in Air Compressors Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1790-1793},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168680},
        abstract = {We present a real-time monitoring and anomaly detection system for air compressors using machine learning algorithms combined with an interactive, web-based dashboard. The proposed system predicts key parameters, including Free Air Delivery (FAD), discharge pressure, discharge temperature, and flow rate using Random Forest regression models. Anomalies are detected based on deviations between predicted and actual values, and are flagged on the dashboard for real-time visualization. The system is implemented using Dash, a Python framework for building interactive web applications. The detected anomalies are saved to a CSV file for further analysis. Our solution provides a cost-effective, scalable method for early fault detection and maintenance planning, ultimately improving the operational efficiency of air compressors.},
        keywords = {Free Air Delivery (FAD), discharge pressure, discharge temperature, and flow rate, Random Forest regression.},
        month = {October},
        }