Monitoring and failure diagnosis of a steel strip process

This paper deals with condition monitoring and failure diagnosis of a steel strip rinsing process. Modeling and identification of the process is based on a priori knowledge about the process and data from the process. In the model, the worn parts are modeled explicitly and estimated online by an extended Kalman filter. The parameter estimation is used for supervision and as an advisory system for the process operators to decide which worn parts should be changed at the next planned stop. In addition to the normal wear, other types of abrupt failures may suddenly occur. It is not possible to detect these failures directly and the failures will give a biased parameter estimate and mislead the process operators into thinking that a part subject to wear should be changed although it is performing well. Therefore, the condition monitoring system is complemented with a fault detection and diagnosis system, which distinguishes normal wear from sudden abrupt failures.