Gas Turbine Machinery Diagnostics: A Brief Review and a Sample Application

Abstract

The intersection of machine learning methods and gas turbine sensor data has expanded rapidly in the last decade to include numerous applications of regression, clustering, and even neural network algorithms. Learning algorithms have pushed traditional engine health management into the realm of prognostic health management. This paper starts with a review of several common computational methods used to monitor the condition of gas turbines currently employed by both industry and academia. Sources of application of machine learning algorithms from outside the gas turbine industry are also brought in. Focus is generally placed on industrial gas turbines with an industry standard monitoring system. The authors explore beyond gas path analysis with a novel use of machine learning algorithms to engine component classification. The paper concludes with a case study of applying learning algorithms to machine data to identify different fuel valves.

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

“…The combination of machine learning methods with information from GPU sensors has expanded rapidly over the past decade and includes numerous programs for regres-sion, clustering, and even neural network algorithms. On this basis, the work [11] provides an overview of generally accepted computational methods used in industry to monitor the technical condition of gas turbines. The paper also provides sources for the application of machine learning algorithms that are not related to the gas turbine industry.…”

Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics

“…However, this approach requires a lot of field data. Algorithms such as support vector machines (SVM) have shown good results in [5,6]. SVM has been used for gas turbine fault detection, where it showed an accuracy greater than 80% for test data and gas generator prognostics.…”