Research on the application of mechanical fault prediction model based on deep learning in manufacturing equipment
基于深度学习的机械故障预测模型在制造设备中的应用研究

摘要

深度学习技术为机械故障预测提供了新的解决方案，特别是在制造设备的维护和管理方面。本研究旨在探讨基于深度学习的机械故障预测模型在制造设备中的应用，通过分析现有模型和技术，评估其在不同应用场景下的有效性。采用深度学习方法可以显著提高故障预测的准确性和可靠性，从而减少停机时间，降低维护成本。本研究还讨论了如何优化这些模型以适应特定工业环境的需求，并提出了一系列改进措施。所提出的模型不仅能够有效地识别潜在故障，还能提供可靠的预防性维护建议。

Abstract

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.