Designing a Deep Autoencoder Neural Network for Detecting Sound Anomalies in Smart Factories Using Unsupervised Learning

Modern world technologies such as the integration of technologies such as the Internet of Things (IoT), cloud computing, and machine learning (ML) enhance the challenges of smart industrial management. Detecting anomalies in predictive maintenance within smart factories, and monitoring machine health to prevent unexpected breakdowns. This research presents an advanced model for designing automatic encoders capable of distinguishing between sounds emitted by machines in industrial environments and identifying faults. The MIMII dataset and advanced feature extraction techniques, such as MFCCs, are adopted as key factors in making the proposed model. The four evaluation measures: accuracy, recall, recall, and F1 score, in addition to the confusion matrix, were also adopted. To evaluate the model's performance. The results confirm the effectiveness and robustness of the proposed deep neural network model designed for autoencoders in the field of artificial audio classification. With a commendable accuracy rate of 93.95% and F1 score of 95.31%,