Condition monitoring of industrial gas turbine critical operating parameters using statistical process control charts

Abstract

This paper presents condition monitoring of industrial gas turbine by monitoring its critical operating parameters using statistical process control. This will consequently enables the detection of any degradation of gas turbine operating parameters and thus to better prepare for any forward actions that required. Basically performance of gas turbine and its critical operating parameters degrades over time. These parameters however degrades and eventually reach the OEM recomended limits without even triggereing any earlier alerts. Therefore, corrective maintenance actions are required to bring the parameters back to an acceptable operating condition which causing downtime in operation and accounts for large maintenance together with operating costs. Hence by identifying any degradation and deviation in gas turbine parameters in advance before it reaches its OEM limit will help to improve maintenance scheduling and practices and thus enhanced the reliability of the machine. It also able to identify false alarms and shutdowns which can cause unnecessary maintenance and non profitable stops. SFC method is also found to be able to estimate the progression of component/ performance degradation and thereby generating a continuously updated prediction of the remaining useful life of machine components. SPC based machine condition monitoring uses statistical process control charts such as individual and moving range methods to create the operating threshold of the machine. These thresholds were showed to be capable to determine and identify performance degradation in advance or earlier before it reaches the OEM limits for each individual parameters.