Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults

Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics, since raw data cleaning represents a key process in the gas turbine industry. To this end, this paper presents a comprehensive approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS), which was previously developed by the authors and has been substantially improved and validated by means of field data. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called improved-DCIDS (I-DCIDS), can identify seven classes of faults, i.e., out of range, stuck signal, dithering, standard deviation, trend coherence, spike, and bias. First, this paper details the I-DCIDS methodology for sensor fault detection and classification. The methodology uses basic mathematical laws that require some user-defined configuration parameters, i.e., acceptability thresholds and windows of observation. Second, a sensitivity analysis is carried out on I-DCIDS parameters to derive some rules of thumb about their optimal setting. The sensitivity analysis is performed on four heterogeneous and challenging datasets with redundant sensors acquired from Siemens gas turbines (GTs). The results demonstrate the diagnostic capability of the I-DCIDS approach in a real-world scenario. Moreover, the methodology proves to be suitable for all types of datasets and physical quantities and, thanks to its optimal tuning, can also identify the exact time point of fault onset.