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

Abstract

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 byK-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.

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation

“…It has since been successfully implemented in many industrial applications. The authors in [ 35 ] used Q contributions to identify faulty sensors in air handling units, and authors in [ 36 ] use both and Q contributions to identify faults that occur in the rolling production of seamless tube process [ 36 ]. Due to its wide use and popularity, this work will consider the CD contributions or contribution plots as a benchmark for comparison.…”