Process Monitoring and Fault Diagnosis for Shell Rolling Production of Seamless Tube

Abstract EN

Continuous rolling production process of seamless tube has many characteristics, including multiperiod and strong nonlinearity, and quickly changing dynamic characteristics.

It is difficult to build its mechanism model.

In this paper we divide production data into several subperiods by K -means clustering algorithm combined with production process; then we establish a continuous rolling production monitoring and fault diagnosis model based on multistage MPCA method.

Simulation experiments show that the rolling production process monitoring and fault diagnosis model based on multistage MPCA method is effective, and it has a good real-time performance, high reliability, and precision.