Gas turbines age and sometimes have problems that may lead to a trip or failure event. This paper describes how one can reliably forecast such problems at a future date so that the problem can be dealt with in the present. These methods are data-driven by relying on machine learning to develop the mathematical description for the gas turbine. First, all instrumentation is combined in a state of the turbine that is forecasted into the future using a long short-term memory (LSTM) network. Second, this forecast is analyzed by a neural network trained to recognize normal healthy behavior in order to identify problem. Third, problems can be diagnosed in some special circumstances by recognizing their fingerprint using past examples of the same failure mode; this is particularly relevant to vibration data.

Keywords:
machine learning, Artificial Intelligence, neural network, deep learning, modeling, predictive maintenance, deviation, failure event, healthy behavior, gas turbine
Subjects:
Information Management and Systems, Artificial intelligence, Neural networks
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2020. International Petroleum Technology Conference
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