Validation of an advanced diagnostic methodology for the identification and classification of gas turbine sensor faults by means of field data

Sensor fault detection is a crucial aspect for raw data cleaning in gas turbine industry. To this purpose, a comprehensive approach for Improved Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named I-DCIDS) was developed by the authors to detect and classify several classes of fault. For single-sensors or redundant/correlated sensors, the I-DCIDS methodology can identify seven classes of fault, i.e. Out of Range, Stuck Signal, Dithering, Standard Deviation, Trend Coherence, Spike and Bias. Since the considered faults are detected by means of a methodology which relies on basic mathematical laws and user-defined parameters, sensitivity analyses are carried out in this paper on I-DCIDS parameters to derive some rules of thumbs about their optimal setting. The sensitivity analyses are carried out on four heterogeneous and challenging datasets with redundant sensors installed on Siemens gas turbines.