Research on AGC Fault Diagnosis of Rolling Mill Based on Deep Confidence Network and Internet of Things Technology
Thickness control is an important factor in determining the quality of steel products,and the performance and status of the mill AGC hydraulic system have a great impact on the quality of the rolling of the target.Using the fault diagnosis method of deep confidence network based on Internet of Things,using the intelligent control of Internet of Things as a platform,using wire-less sensing network technology to carry out measurement and control of all kinds of data and remote sharing,the collected fault signal input depth confidence network model,training the sample data,and accurately diagnosing the fault through experimen-tal simulation.After experiments,the deep confidence network model can be applied to the complex system such as mill AGC,which can ensure the normal operation of the system and reduce the failure rate of equipment while improving production effi-ciency.
Hydraulic AGCInternet of ThingsDeep Confidence NetworkFault DiagnosisRolling QualityIn-telligent Control
万方数据
维普
