Christer Karlsson

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

Abstract 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.

New Methods for Adaptation to Degeneration in Process Models for Process Industries

The implementation of model-based control and diagnostics suffer strongly from the fact that models deteriorate as a function of process and sensor deterioration. Also, changes in the raw material (i.e. wood) may occur and often the process control is not addressing these variations in reality. It is thus vital for the model system to be robust in the sense that it is transparent and easy for the operator to maintain. Robustness is essential in many parts of the system, including measurement, process model validation, the ability of the model to adapt to changes in the process, optimization algorithms, and of course the model itself. In this paper, we first show three real-life applications of the utilization of models for diagnostics and control. Thereafter conditions for on-line adaptation of the models are discussed. The challenges when designing such a system are in achieving operator confidence, filtering of misleading measured data, adaptation of process parameters when the process parameters change, and combining validation of measurements and process models. These challenges are met by using a combination of physical and statistical models and methods based on them such as model predictive control (MPC) and parameter estimation. The model should be maintained by a qualified engineer who should be able to explain the system to the operator so that it is understood and confidence can be maintained.

Integrated Process Control, Fault Diagnostics, Process Optimization and Production Planning - Industrial IT

ABSTRACT In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Visy Pulp and Paper mill in Tumut, Australia.