Abstract
A gas turbine trip is an unplanned shutdown, of which the consequences are business interruption and a reduction of equipment remaining useful life. Therefore, detection and identification of symptoms of trips would allow predicting its occurrence, thus avoiding damages and costs.
The development of machine learning models able to predict gas turbine trip requires the definition of a set of target data and a procedure of feature engineering that improves machine learning generalization and effectiveness.
This paper presents a methodology that focuses on the steps that precede the development of a machine learning model, i.e., data selection and feature engineering, which are the key for a successful predictive model.
Data selection is performed by partitioning units into homogeneous groups according to different criteria, e.g., type, region of installation, and operation. A subsequent matching algorithm is applied to rotational speed data of multiple gas turbine units to identify start-ups and shutdowns so that the considered units can be partitioned according to their operation, i.e., base load or peak load.
Feature engineering aims at creating features that improve machine learning model accuracy and reliability. First, the Discrete Fourier Transform is used to identify and remove from the time series the seasonal components, i.e., patterns that repeat with a given periodicity. Then, new features are created based on gas turbine domain knowledge in order to capture the complex interactions among system variables and trip occurrence.
The outcomes of this paper are the definition of a set of target examples and the identification of a meaningful set of features suitable to develop a machine learning model aimed at predicting gas turbine trip.
Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol
