Unsupervised Methodology for the Prognostics of Gas Turbine Abrupt Faults

The current energy market requires that gas turbines (GTs) run efficiently and reliably, thus improving their sustainability. To this aim, condition monitoring is fundamental for GT manufacturers to track performance degradation and ensure fault detection in advance. Thanks to the huge amount of data acquired during GT operation, data-driven methodologies can be successfully employed for fault detection and prediction. Therefore, this paper develops an unsupervised data-driven methodology for the prognostics of GT abrupt faults. The methodology comprises i) an autoencoder (AE) neural network aimed at modeling GT operation in the absence of fault symptoms and ii) a threshold-based criterion for the detection of anomalous deviations between actual data and AE predictions. A sensitivity analysis on the anomaly threshold is carried out to identify the optimal set-up of the methodology. The paper considers GT trip as archetypal of an abrupt fault. Since no labeled data are available in the case of trip occurrence, the time point at which trip symptoms arise has to be identified. Furthermore, the paper also investigates which measurable variables are most effective for predicting GT trip, in order to provide useful insights on trip prediction. The methodology is validated by considering a real-world case study comprising data collected from six GTs before trip occurrence. The results prove that the optimally tuned methodology correctly classifies the 80% of data. Another relevant result is that fuel mass flow rate, exhaust gas temperature, and compressor discharge temperature are the most useful measurable variables for the detection of trip symptoms.