Detection and interactive isolation of faults in steam turbines to support maintenance decisions

The maintenance of steam turbines is expensive, particularly if dismantling is required. A concept for the provision of support for the maintenance engineer in determining steam turbine status in relation to the recommended maintenance interval is presented here. The concept embodies an artificial neural network which is conditioned to recognise patterns known to be related to faults. The faults simulated are not known to be recognized on-line and the concept is in an early stage of development. An example of a Bayesian network structure containing expert knowledge is proposed to be used, in a dialogue with the operator, to isolate the root causes of a number of fault types. The aim is to be well informed about the statue of the turbine in order to take earlier and better informed maintenance actions. The detection procedure has been validated in a simulation environment.