One of the most disrupting events that affect gas turbine (GT) operation is trip, since its occurrence reduces machine life span and also causes business interruption. Thus, early detection of incipient symptoms of GT trip is crucial to ensure efficient operation and save costs.
This paper presents a data-driven methodology of which the goal is the disclosure of the onset of trip symptoms by exploring multiple trigger scenarios. For each scenario, a time window of the same length is considered before and after the trigger time point: the former is supposed to be representative of normal operation and is labeled “No trip”, whereas the latter is labeled “Trip”. A Long Short-Term Memory (LSTM) neural network is first trained for each scenario and subsequently tested on new trips over a timeframe of three days of operation before trip occurrence. 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 GTs in operation. Data collected from multiple sensors are employed and analyzed.
The methodology provides the most likely trigger position for four clusters of trips and both case studies with a confidence in the range 66% - 97%.