Journal of Engineering Research

Deep learning technology provides a new solution for mechanical fault prediction, especially in the maintenance and management of manufacturing equipment. This study aims to explore the application of mechanical fault prediction models based on deep learning in manufacturing equipment. By analyzing existing models and technologies, the effectiveness of these models in different application scenarios is evaluated. The use of deep learning methods can significantly improve the accuracy and reliability of fault prediction, thereby reducing downtime and lowering maintenance costs. This study also discusses how to optimize these models to meet the requirements of specific industrial environments and proposes a series of improvement measures. The proposed model can not only effectively identify potential faults but also provide reliable preventive maintenance suggestions.

Deep Learning; Mechanical Fault Prediction; Manufacturing Equipment; Maintenance Cost