Multi-fault diagnosis scheme based on robust nonlinear observer with application to rolling mill main drive system

Abstract

A fault diagnosis solution is proposed in this article for the uncertainty and external disturbance problems in the main drive system of a rolling mill based on analytical redundancy. Considering the nonlinear friction damping, a system is mathematically modeled, and a bank of nonlinear Luenberger observers is designed. The generalized residual set theory is used to isolate two typical fault types of the system. To optimize the anti-disturbance ability of the observer, the disturbance attenuation factor is introduced. The influence of external disturbance on fault estimation is reduced by adjusting the disturbance attenuation factor. Using Lyapunov stability theory, the convergence analysis of the observer dynamic error equation is provided. The observer gain is obtained through resolving the optimality issue under the linear matrix inequality constraint. Numerical simulations of a 2030-mm tandem cold rolling mill were conducted to demonstrate the practicability of the approach. This fault diagnosis scheme is important to improve the stability of system operation.