This work deals with modelling the vibration response of a gas turbine obtained during the start‐up process until reaching the nominal speed for power generation. Analysing the vibrations of a complex systems like a gas turbine is useful for the diagnostic of faults or damages in the internal mechanical components of the different stages that integrate a turbine. This work focuses on the study of the shaft vibrations of the bearing radial type mounted between the shaft and the bearing compressor associated with the speed of the turbine. This relationship is studied using experimental data collected from a particular gas turbine model. In particular, we propose a methodology to synthesize a computational model following a supervised learning approach implemented through different machine learning techniques, including a multi‐layers perceptron network, support vector machine (SVM), random forest (RF) and genetic programming (GP) with local search. Results show that SVM, RF and GP perform very well in this task, producing accurate predictive models. Moreover, there are some interesting trade‐offs between the methods, regarding generalization error, overfitting and model interpretability that are relevant for future applications and research.

中文翻译：

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使用机器学习对燃气轮机的振动响应进行建模

这项工作涉及对在启动过程中获得的燃气轮机的振动响应进行建模，直到达到发电的额定速度为止。分析诸如燃气轮机之类的复杂系统的振动对于诊断集成了涡轮机的不同级的内部机械组件中的故障或损坏很有用。这项工作的重点是研究与涡轮速度相关的安装在轴和轴承压缩机之间的轴承径向型轴振动。使用从特定燃气轮机模型收集的实验数据研究了这种关系。特别是，我们提出了一种通过通过不同的机器学习技术实施的监督学习方法来合成计算模型的方法，包括多层感知器网络，支持向量机（SVM），随机森林（RF）和带有本地搜索的遗传规划（GP）。结果表明，SVM，RF和GP在此任务中表现出色，生成了准确的预测模型。此外，在方法之间存在一些有趣的折衷，涉及泛化误差，过度拟合和模型可解释性，这些都与将来的应用和研究相关。