Ramanaa Y, P. Vanathi, Vijayakumar R, Vishwa G, Prakash M S
convolutional neural networks, recurrent neural networks
This conference paper introduces a cutting-edge approach for real-time fault identification in electronic circuitry, leveraging the power of deep learning techniques. The proposed system aims to enhance the reliability and efficiency of fault detection in various electronic systems by employing a robust deep learning model. Traditional fault identification methods often face challenges in providing timely alerts and accurate diagnosis, which can result in prolonged downtime and increased maintenance costs. The proposed system integrates a state-of-the-art deep learning architecture trained on extensive datasets containing diverse fault scenarios. This enables the model to learn complex patterns and correlations associated with different types of faults in electronic circuits. The use of deep learning not only enhances the accuracy of fault detection but also facilitates real-time processing, making it suitable for dynamic and fast-paced environments. To ensure immediate response to identified faults, an alert system has been seamlessly integrated into the circuitry. Upon detection of any anomaly, the system triggers an instant alert, enabling swift action to rectify the issue and minimize downtime. The alert system utilizes advanced communication protocols to notify relevant personnel or control systems, ensuring a rapid and effective response. Key features of the proposed system include adaptability to various circuit configurations, scalability to handle large-scale systems, and the ability to continuously learn and improve through feedback loops. The integration of this deep learning-based fault identification system with an immediate alert mechanism represents a significant advancement in the field of circuit diagnostics, promising enhanced reliability, reduced maintenance costs, and improved overall system performance.
Article Details
Unique Paper ID: 162604

Publication Volume & Issue: Volume 10, Issue 10

Page(s): 531 - 535
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