Anomaly Detection and Failure Prediction in Gas Turbines

This study is based on time-series data taken from the combined cycle heavy-duty utility gas turbines. For analysis, first, a multi-stage vector autoregressive model is constructed for the nominal operation of powerplant assuming sparsity in the association among variables, and this model is used as a basis for anomaly detection and prediction. This prediction is compared with the time-series data of the powerplant test data containing anomalies. Granger causality networks, which are based on the associations between the time series streams, can be learned as an important implication from the vector autoregressive modelling. This method suffers from the disadvantage that some of the variables are not stationary even after segmenting the working mode based on the RPM. To improve the efficacy of the algorithm, the observations are further clustered into different working modes, because of the heterogeneous behavior of the gas turbine parameters under various modes. Then predicting the operational parameters is considered under each mode respectively, via algorithms including random forest, generalized additive model, and neural networks. The comparative advantage based on prediction accuracy and applicability of the algorithms is discussed for real-time use and post processing. The advantage of this segmentation method is that it achieves high predictive power and provides insight into the behavior of specific gas turbine variables. Next, the long-memory behavior of residuals is modeled, and heterogeneous variances are observed from the residuals of the generalized additive model. Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are employed to fit the residual process, which significantly improve the prediction. Rolling one-step-ahead forecast is studied. Numerical experiments of abrupt changes and trend in the blade-path temperature are performed to evaluate the specificity and sensitivity of the prediction. The prediction is sensitive given reasonable signal-to-noise ratio and has lower false-positive rate