Application of deep learning in noise detection of electromechanical products

In recent years, machine learning (ML) techniques have been widely integrated into manufacturing processes. This study explores supervised learning-based machine learning models designed for noise detection. The approach involves feature engineering, extracting relevant features from signals to provide meaningful inputs for machine learning models. Various algorithms, including convolutional neural networks (CNN) and other deep learning architectures, are employed to capture complex dependencies within the data. Training processes involve optimization techniques to enhance model performance and generalizability. Performance evaluation on benchmark datasets compares the proposed model with existing noise detection methods. Results demonstrate the outstanding accuracy and robustness of the generated machine learning model in distinguishing noise from clear signals. Additionally, the study explores the transferability of these models to new scenarios, highlighting their adaptability in practical applications. Findings from this research contribute to advancing noise detection methods, showcasing the effectiveness of machine learning in addressing this crucial challenge. The proposed solution is cost-effective, structurally simple, and holds vast potential for widespread implementation in factory settings in the future.