远程监控及故障诊断技术在大型轧机系统中的应用

Application of Remote Monitoring and Fault Diagnosis Technology in Large Rolling Mill Systems

随着中国工业经济的快速发展,对板带材的需求量日益增加,国内外板带材生产线的建设也在逐年增加。然而,大型轧机系统的复杂性和恶劣的工作环境使得设备故障诊断变得越来越困难。传统的故障诊断技术缺乏智能化和实时性。因此,基于设备运行状态的实时诊断,并通过人机界面及时反馈的远程监控及故障诊断技术成为当前自动化生产线领域的热门课题。为应对这一挑战,研发出了大型轧机故障诊断专家系统。该系统融合了BP神经网络、贝叶斯网络和传统的专家系统,采用非自动型和主动型相结合的知识获取方式构建专家知识库,并建立完善的推理机制。通过将该故障诊断模型应用于生产线,可以实现对大型轧机运行状态的实时监控,并进行故障的诊断与预防。这种技术的应用可以简化板带材生产线的故障诊断流程,提高生产线的效率和稳定性。

With the rapid development of China's industrial economy,the demand for strip materials is increasing,and the construction of strip production lines both domestically and internationally is growing year by year.However,the complexity of large-scale rolling mill systems and the harsh working environment make equipment fault diagnosis increasingly challenging.Traditional fault diagnosis techniques lack intelligence and realtime capability.Therefore,real-time diagnosis based on equipment operating status,combined with remote monitoring and fault diagnosis technology that provides timely feedback through human-machine interfaces,has become a hot topic in the field of automated production lines.To address this challenge,a large-scale rolling mill fault diagnosis expert system was developed.This system integrates BP neural networks,Bayesian networks,and traditional expert systems.It employs a combination of manual and active knowledge acquisition methods to build an expert knowledge base and establishes a comprehensive reasoning mechanism.By applying this fault diagnosis model to the production line,real-time monitoring of the operating status of the large-scale rolling mill and diagnosis and prevention of faults can be achieved.The application of this technology can simplify the fault diagnosis process of strip production lines,improving the efficiency and stability of the production line.