Gas turbine trip prediction with time-Series data using RNN and LSTM.

Abstract

Gas turbine (GT) trip is one of the most disruptive occurrences that influenc eG Toperation, as it reduces the remaining useful life of the equipment and results in revenue loss due to business interruption. Thus, early diagnosis of early GT trip symptoms is critical for ensuring effective operation and lowering operating and maintenance expenses. In this work, we implement two neural network methods, RNN and LSTM, for gas turbine trip prediction using a time series sensors readings dataset and compare their performances in accurately predicting gas turbine trips within 60 seconds of their occurrence, allowing operators to take timely and effective actions to prevent trips and ensure the reliability and efficienc yo fpowe rgeneration systems. The objective of this work is to defin eth ebes tperformin gmode lfo rthi ssensitiv etas kand reach the highest possible accuracy and precision by implementing different architectures and exploring variations of hyperparameters such as the number of features, validation split, and the input sequence length. Our experimental results show that both RNN and LSTM are effective in achieving the goal of predicting gas turbine trips prior to their occurrence. The best-performing model is the bidirectional LSTM with multiple features input, a sequence length of 50, and 10% validation split, where we reached a test accuracy of 96.47%, precision 97.14%, recall 94.44%, F1 score 95.77%, and ROC-AUC of 0.96.