A STATE-SPACE CONDITION MONITORING MODEL FOR FURNACE EROSION PREDICTION AND REPLACEMENT

The paper develops a replacement action decision aid for a key furnace component subject to condition monitoring. A state space model is use d to predict the erosion condition of the inductors in an induction fu rnace in which a measure of the conductance ratio (CR) is used to indi rectly assess the relative condition of the inductors, and to guide re placement decisions. This study seeks to improve on this decision proc ess by establishing the relationship between CR and the erosion condit ion of the inductors. To establish such a relationship, a state space model has been established and the system parameters estimated from CR data. A replacement cost model to balance at any time costly replacem ents with possible catastrophic failure is also proposed based upon th e predicted probability of inductor erosion conditional upon all avail able information. The well known Karman filter is employed to derive t he predicted and updated probability of inductor erosion level conditi onal upon CR data to date. This is the first time the condition monito ring decision process has been modelled for real plant based upon filt ering theory. The model fits the data well, gives a sensible answer to the actual problem, and is transferable to other condition monitoring contexts. Possible extensions are discussed in the paper. (C) 1997 El sevier Science B.V.