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@article{204519,
        author = {Adarsh J and Aparna Satheesh J and Gokul G S and Basil B Kumar and Resmi S R},
        title = {AI-Based Acoustic Emission Monitoring System for Transformer Fault Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {197-202},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204519},
        abstract = {Power transformers are critical components in electrical power systems, and their unexpected failure can lead to major power outages and economic losses. Conventional transformer monitoring techniques such as Dissolved Gas Analysis (DGA) and infrared thermography are costly and mostly offline. This paper presents the design and implementation of an AI-based acoustic emission monitoring system for transformer fault detection using an ESP32 microcontroller. The system utilizes a non-contact MEMS microphone for acoustic signal acquisition, a vibration sensor for mechanical disturbance detection, and environmental sensors for monitoring temperature and humidity conditions. Signal processing techniques such as Root Mean Square (RMS) and Zero Crossing Rate (ZCR) are used for feature extraction, and machine learning algorithms classify faults into electrical, mechanical, or normal conditions. The proposed system enables real-time monitoring, IoT-based visualization, and early fault warning, thereby improving transformer reliability and reducing maintenance costs.},
        keywords = {Acoustic Emission, Transformer Fault Detection, ESP32, IoT Monitoring, Machine Learning},
        month = {June},
        }