Anomaly detection and classification is a key challenge for gas turbine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) was developed by the authors in previous papers. The methodology consists of an Anomaly Detection Algorithm (ADA) and an Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering. Anomalies are subsequently analyzed by the ACA to perform their classification, according to time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified.
The performance of the DCIDS approach is assessed in this paper based on a significant amount of field data taken on several Siemens gas turbines in operation. The field data refer to six different physical quantities, i.e. vibration, pressure, temperature, VGV position, lube oil tank level and rotational speed. The analyses carried out in this paper allow the detection and classification of the anomalies and provide some rules of thumb for field operation, with the final aim of identifying time occurrence and magnitude of faulty sensors and measurements.