摘要
传统的机械自动化系统故障诊断方法大多依赖于人工经验和专家知识,这些方法存在诸多局限性,如诊断效率低下、准确性不高等。针对该问题,将深入探讨深度学习在机械自动化系统故障诊断中的应用,首先采集与预处理机械数据,并将这些数据构建与训练卷积神经网络模型,将训练好的模型部署与生产环境中,用于识别与分类故障特征。利用实验结果表明,所提方法能够快速、准确识别机械自动化系统故障,可为智能制造和工业互联网的发展提供有力的技术支持。
Most of the traditional fault diagnosis methods of mechanical automation system rely on manual experience and expert knowledge.These methods have many limitations,such as low diagnosis efficiency and low accuracy.In view of this problem,this paper will discuss the application of deep learning in fault diagnosis of mechanical automation system.First,this paper will collect and preprocess mechanical data,build and train the convolutional neural network model,and deploy the trained model and production environment to identify and classify fault features.Finally,the experimental results show that the method proposed in this paper can quickly and accurately identify the faults of mechanical automation system,and can provide strong technical support for the development of intelligent manufacturing and industrial Internet.
作者
吴建进
Wu Janjin(Yunnan Ronghe Investment Holdings Co.,Ltd.,Kunming Yunnan 650000,China)
出处
《机械管理开发》
2025年第6期55-57,共3页
Mechanical Management and Development
关键词
深度学习
自动化系统
故障诊断
deep learning
automation system
fault diagnosis
