Authors: Mrs. V. Suvarna, Mavuri Bhuvana, Boddu Rajeev, Choppella Vamsi Kumar, Dhulipudi Sree Vivek
Abstract: To improve prediction accuracy and enable real-time monitoring of mechanical components, a deep learning-based approach is proposed. The system utilizes a Convolutional Neural Network (CNN) to extract important features from mechanical parts using sensor data. These features are further processed through fully connected layers for information fusion and classification, allowing accurate prediction of remaining useful life and health status. The trained deep learning model is integrated into a monitoring system to create a complete framework for continuous condition monitoring and life prediction. The system is further optimized to enhance prediction accuracy, real-time performance, and adaptability under different working and environmental conditions. Experimental results show that the proposed model achieves high performance with a Mean Absolute Error (MAE) of 2.1, Root Mean Squared Error (RMSE) of 2.5, and Mean Absolute Percentage Error (MAPE) of 10%. These results demonstrate the effectiveness of the approach and its potential for practical applications in industrial maintenance and reliability improvement.
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