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

By Weizhong Yan, Lijie Yu. 2015

153 citations recorded by the source corpus.

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 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 combustors’ 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 combustors’ anomaly detection performance.Deep learning, one of the breakthrough technologies in machine learning, has attracted tremendous research interests in recent years in the domains such as computer vision, speech recognition and natural language processing.Deep learning, to the best of our knowledge, has not been used for any PHM applications, however. It is our hope that our initial work presented in this paper would shed some light on how deep learning as an advanced machine learning technology can benefit PHM applications and, more importantly, can stimulate more research interests in our PHM community.

Subfields: Artificial Intelligence, Computational Mechanics, Environmental Engineering

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Yan and Yu [149] have also used SdA joint with extreme”
Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing (2018, methodology): “Anomaly detection has leveraged DL algorithms as a feature extraction in [14].”
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, methodology): “Yan and Yu [121] used Deep Learning tools for detecting abnormal behavior and incipient faults in industrial turbines where failures can be catastrophic and extremely costly.”
Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, background): “cted features. As the number of monitored parameters increases so does the diculty of feature engineering for diagnostics engineers and consequently there is an interest in automating this processes [8] or circumventing the need for feature engineering altogether. Deep learning (DL) has the potentia”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, unknown): “[177] [178] [179] [180] [181] [182] [183] [184] [185] [186] [187] [188]”