One-Class Extreme Learning Machines for Gas Turbine Combustor Anomaly Detection

Gas turbine combustor anomaly detection plays a critical role in reducing operation and maintenance costs in power plant operations. One-class classification, to learn a model that properly describes the normal samples, is one of many anomaly detection approaches that have shown promising performance in real-world applications. Extreme learning machines (ELMs), a recent developed machine learning technique, have outperformed SVMs and other machine learning methods for many machine learning problems. ELMs have been originally used for binary or multiclass classification and regression as well. Using ELM for one-class classification has emerged very recently, but has not been widely explored for real world applications. In this paper, we adopt ELMs to a new application domain - industrial machine condition monitoring. More specifically, we apply one-class ELMs for more accurate anomaly detection of gas turbine combustors. We evaluate different one-class ELMs and compare them against other one-class classifiers. Using a real-world gas turbine combustor anomaly detection as the case study, we demonstrate that one-class ELMs can be more effective than other one-class classification algorithms in early detecting gas turbine combustor anomalies.