Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

Nowadays, the importance of gas turbine monitoring and diagnostics pushes OEM operators to exploit historical data to search for early indicators of incipient failures. One of the most disrupting events that affect GT operation is gas turbine trip, since its occurrence causes a reduction of equipment remaining useful life as well as revenue loss because of business interruption. Thus, early detection of incipient symptoms of gas turbine trip is crucial to ensure efficient operation and lower operation and maintenance costs. This paper presents a data-driven methodology aimed at investigating and disclosing the onset of trip symptoms. The goal of the methodology is the identification of the time point at which trip symptoms are triggered, by exploring multiple scenarios characterized by different trigger positions. For each scenario, a time window of the same length is considered before and after the trigger time point. For classification purposes, the former is supposed to be representative of normal operation and thus is labeled as “No trip”, whereas the latter is labeled as “Trip”. A Long Short-Term Memory (LSTM) neural network is employed as the classification model. A predictive model is first trained for each scenario and subsequently tested on new observations (i.e., trip events) by considering the whole available timeframe. Finally, trips are clustered into homogeneous groups according to their most likely trigger position, which identifies the time point of onset of trip symptoms. The methodology is applied to two real-world case studies composed of a collection of trips, of which the causes are different, taken from various fleets of Siemens gas turbines. Data collected from multiple sensors during three days of operation before trip occurrence are employed and analyzed. The methodology provides the most likely trigger position for four clusters of trips within the two days before trip occurrence with a confidence in the range 66% - 97%.