A novel framework of process monitoring and fault diagnosis for steel pipe hot rolling

Taking the continuous rolling process of seamless steel pipes as research object, a monitoring model for the continuous rolling process of seamless steel pipes based on the kernel entropy component analysis method is established, and abnormal production situations and quality problems are diagnosed based on the kernel space fault contribution rate algorithm. Through process monitoring and fault diagnosis experiments on simulation data and seamless steel pipe onsite production data, the experimental results demonstrated the effectiveness of the proposed method in the field. Compared with common process monitoring methods, the actual production data results show that the kernel entropy component analysis–dissimilarity method has better monitoring performance, with an FDR of 97.0% and a FAR of 1.0%. Finally, a process monitoring and fault diagnosis system for the production site is developed for producing abnormal alarms.