Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

作     者：Feiyang Cao 

作者机构：Zhengzhou Business UniversityZhengzhou 451200China 

出 版 物：《Journal of Electronic Research and Application》 (电子研究与应用)

年 卷 期：2024年第8卷第4期

页      面：108-114页

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Intelligent manufacturing Machine tool fault diagnosis Predictive maintenance Big data Machine learning System integration 

摘      要：In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational *** maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing ***,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool ***,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine ***,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing ***,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing *** study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.