Prognostics for advanced compressor health monitoring

Axial flow compressors are subjected to demands for ever-increasing levels of pressure ratio at a compression efficiency that augments the overall cycle efficiency. However, unstable flow may develop in the compressor, which can lead to a stall or surge and subsequently to gas turbine failure resulting in significant downtime and cost to repair. To protect against these potential aerodynamic instabilities, compressors are typically operated with a stall margin. This means operating the compressor at less than peak pressure rise which results in a reduction in operating efficiency and performance. Therefore, it is desirable to have a reliable method to determine the state of a compressor by detecting the onset of a damaging event prior to its occurrence. In this paper, we propose a health monitoring scheme that gathers and combines the results of different diagnostic tools to maximize the advantages of each one while at the same time minimizing their disadvantages. This fusion scheme produces results that are better than the best result by any one tool used. In part this is achieved because redundant information is available that when combined correctly improves the estimate of the better tool and compensates for the shortcomings of the less capable tool. We discuss the usage of diagnostic information fusion for a compressor event coupled with proactive control techniques to support improved compressor performance while at the same time avoid the increased damage risk due to stall margin reduction. Discretized time to failure windows provide event prediction in a prognostic sense.