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Faults detection and identification for gas turbine using DNN and LLM

机译：使用DNN和LLM的燃气轮机故障检测与识别

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摘要

Applying more features gives us better accuracy in modeling; however, increasing the inputs causes the curse of dimensions. In this paper, a new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems. This structure consist of two structure. The first part includes Auto-Encoders (AE) as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features. The second part consists of the Local Model Networks (LMNs) with LOcally Linear MOdel Tree (LOLIMOT) algorithm to model outputs (multiple models). The fault detection is based on these multiple models. Hence the residuals generated by comparing the system output and multiple models have been used to alarm the faults. To show the effectiveness of the proposed structure, it is tested on single-shaft industrial gas turbine prototype model. Finally, a brief comparison between the simulated results and several related works is presented and the well performance of the proposed structure has been illustrated.
机译：应用更多功能可以使我们在建模中具有更高的准确性；然而，增加投入会导致规模的诅咒。本文提出了一种用于高维系统故障检测与识别（FDI）的新结构。该结构由两个结构组成。第一部分包括作为深度神经网络（DNN）的自动编码器（AE），以产生特征工程过程并总结特征。第二部分包括具有局部线性模型树（LOLIMOT）算法的局部模型网络（LMN），用于对输出进行建模（多个模型）。故障检测基于这些多个模型。因此，通过比较系统输出和多个模型而生成的残差已用于警告故障。为了显示所提出结构的有效性，在单轴工业燃气轮机原型模型上对其进行了测试。最后，给出了模拟结果与一些相关工作的简要比较，并说明了所提出结构的良好性能。

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