A Mixed Data-Driven and Model Based Fault Classification for Gas Turbine Diagnosis

Abstract

In modern gas turbine health monitoring systems, the diagnostic algorithms based on gas path analysis may be considered as principal. They analyze gas path measured variables and are capable of identifying different faults and degradation mechanisms of gas turbine components (e.g. compressor, turbine, and combustor) as well as malfunctions of the measurement system itself. Gas path mathematical models are widely used in building fault classification required for diagnostics because faults rarely occur during field operation. In that case, model errors are transmitted to the model-based classification, which poses the problem of rendering the description of some classes more accurate using real data. This paper looks into the possibility of creating a mixed fault classification that incorporates both model-based and datadriven fault classes. Such a classification will combine a profound common diagnosis with a higher diagnostic accuracy for the data-driven classes.A gas turbine power plant for natural gas pumping has been chosen as a test case. Its real data with cycles of compressor fouling were used to form a data-driven class of the fouling. Preliminary qualitative analysis showed that these data allow creating a representative class of the fouling and that this class will be compatible with simulated fault classes.A diagnostic algorithm was created based on the proposed classification (real class of compressor fouling and simulated fault classes for other components) and artificial neural networks. The algorithm was subjected to statistical testing. As a result, probabilities of a correct diagnosis were determined. Different variations of the classification were considered and compared using these probabilities as criteria. The performed analysis has revealed no limitations for realizing a principle of the mixed classification in real monitoring systems.'Telephone and fax

Adaptive Gas Path Modeling in Gas Turbine Health Monitoring

“…The works of Loboda and Yepifanov, (2010);Donald, et al, (2008);Loboda, (2008);Fast, et al, (2009);; Romesis and Mathioudakis, (2003); Roemer and Kacprzynski, (2000); Volponi, et al, (2003); Kamboukous and Mathioudakis, (2005) on gas path analysis in condition monitoring, were also critically considered to actualize this task. More so, trend monitoring as utilized in Bently, et al, (2002) and Uhumnwangho, et al, (2003) was also seen as a viable tool for this package.…”

“…Some areas where GTs are used include electric power, petrochemical, mining, marine, air craft, onshore and offshore oil and gas industries. (Ogbonnaya (2004a), Ogbonnaya (2004b), Pussey (2007), Brun and Kurz (2007), Rieger, et al, (1990), Ogbonnaya (2009), Ogbonnaya andTheophilus-Johnson (2010), Loboda and Yepifanov (2010), Aretakis, et al, (2010)). This chapter is therefore timely for these maritime organisations to adopt the proactive measures being proferred to prevent their equipment from catastrophic downtime.…”

Gas Turbine Condition Monitoring and Diagnostics

“…Unfortunately, it is much more difficult to make more accurate the other dependency 2 () Yf =Θ because faults rarely occur. The study presented in (Loboda & Yepifanov, 2010) shows that differences between real and simulated faults can be visible. As mentioned before, the thermodynamic model for steady states has wide application in gas turbine diagnostics.…”

A Model-Based Mixed Data Approach for Optimizing the Performance of an Offshore Gas Turbine Compressor

“…Computing the deviations of measured quantities from their baseline values are determined in a common preliminary stage diagnostic approach before GT health monitoring (Loboda et al 2010;Ogbonnaya et al 2010). The deviations are usually obtained by comparing the engine design value (reference) and operational value (actual).…”