On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach

Weizhong Yan and Lijie Yu
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_15_025.pdf703.89 KBAugust 20, 2015 - 12:35pm

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 combustors’ 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 real-world anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving the anomaly detection performance, in this paper we introduce recently developed deep learning (DL) in machine learning into combustors’ anomaly detection. 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 classifier for performing combustor anomaly detection. Since such deep learned features better capture complex relations among all sensor measurements and the underlying combustors’ behavior than handcrafted features, we expect the deep 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 demonstrate the proposed deep learning based anomaly detection significantly improve combustors’ anomaly detection performance.
Deep learning is considered as one of the breakthrough technologies in machine learning and has attracted tremendous research interests in recent years in the domains such as computer vision, speech recognition and text analytics. Deep learning, to the best of our knowledge, has not been used for any PHM applications, however. Our initial work presented in this paper can hopefully shed some light on how deep learning as an advanced machine learning technology can benefit PHM applications and stimulate more research interests in our PHM community.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
025
Page Count: 
8
Submission Keywords: 
anomaly detection
gas turbine combustors
deep learning
feature learning
feature engineering
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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