Fault Tolerant Supervision of an Industrial Gas Turbine

Supervision of the performance of an industrial gas turbine is important since it gives valuable information of the process health and makes efficient determination of compressor wash intervals possible. Slowly varying sensor faults can easily be misinterpreted as performance degradations and result in an unnecessary compressor wash. Here, a diagnostic algorithm is carefully combined with non-linear state observers to achieve fault tolerant performance estimation. The proposed approach is evaluated in an experimental case study with six months of measurement data from a gas turbine site. The investigation shows that faults in all gas path instrumentation sensors are detectable and isolable. A key result of the case study is the ability to detect and isolate a slowly varying sensor fault in the discharge temperature sensor after the compressor. The fault is detected and isolated before the wash condition of the compressor is triggered, resulting in fault tolerant estimation of compressor health parameters.