  Monitoring gas turbine combustors health, in particular, early detecting
abnormal behaviors and incipient faults, is critical in ensuring gas turbines
operating efficiently and in preventing costly unplanned maintenance. One
popular means of detecting combustor abnormalities is through continuously
monitoring exhaust gas temperature profiles. Over the years many anomaly
detection technologies have been explored for detecting combustor faults,
however, the performance (detection rate) of anomaly detection solutions
fielded is still inadequate. Advanced technologies that can improve detection
performance are in great need. Aiming for improving anomaly detection
performance, in this paper we introduce recently-developed deep learning (DL)
in machine learning into the combustors anomaly detection application.
Specifically, we use deep learning to hierarchically learn features from the
sensor measurements of exhaust gas temperatures. And we then use the learned
features as the input to a neural network classifier for performing combustor
anomaly detection. Since such deep learned features potentially better capture
complex relations among all sensor measurements and the underlying combustor
behavior than handcrafted features do, we expect the learned features can lead
to a more accurate and robust anomaly detection. Using the data collected from
a real-world gas turbine combustion system, we demonstrated that the proposed
deep learning based anomaly detection significantly indeed improved combustor
anomaly detection performance.
