This paper describes a selection of Baker Hughes, a GE company (BHGE) activities to support Gas Turbine (GT) design and operation from simple to more elaborate applications of Machine Learning (ML).

In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.

The paper presents an overview of Baker Hughes digital framework for a predictive maintenance service boosted by Machine Learning and asset knowledge, applied to turbomachinery assets. Optimization of the maintenance scenario is performed through a risk model that assesses online health status and probability of failure, by detecting functional anomalies and aging phenomena and evaluating their impact on asset serviceability. Turbomachinery domain knowledge is used to create physics-based models, to configure a severity assessment layer and to properly map maintenance actions to anomaly types. The implemented analytics framework is able also to forecast engine behaviour over the future in order to optimize asset operation and maintenance, minimizing downtime and residual risk. Predictive capabilities are optimized thanks to the hybrid approach, where physics-based knowledge empowers long term prediction accuracy while data-driven analytics ensure fast-events prognostics. Accuracy of the hybrid approach is a differentiator for maintenance optimization, allowing activities to be planned properly and in early advance with respect to outage execution.