Development of big data lean optimisation using different control mode for Gas Turbine engine health monitoring

Abstract

In the era of big data lean optimisation, a technologically advanced tool for Gas Turbine (GT) performance analysis is necessary. This need is due to big data taken every 5 min of about 4-years, totalling about 6 million data points (1.5×4 data points) for a single unit of GE 7FA engine of 211MW capacity. The use of actual engine data to quantify degradation using a time-base is limited and challenging. This restraint is due to the influence of varying ambient conditions, compressor inlet conditions with different power settings and loads. This study presents a software development kit (SDK) for real-time processing and evaluation of engine data using different control modes with a statistical method of filtering the data using the inlet guide vane (IGV) opening. The study also examines the analysis of GT health monitoring using time-based machine-generated data. The output of this investigation includes obtaining an average degradation trend line for the power output and heat rate increase as a function of time that serves as the basis for the investigation of the economic analysis. Implementing the SDK has reduced the labour cost, enhanced the accuracy of the analysis, and improved the efficiency of service delivery. The validation of the result with the available literature indicated a fair deviation of 1.4%. The utilisation of data for fuel flow recorded for the standard and extended correction was 24.3% and 16.3% at a range of 9.06–9.43 kg/s and 8.95–9.31 kg/s, respectively. The average percentage of fouling degradation level amounted to a 7.2% reduction of power output and a 1.6% increase in heat rate for one year of operation.