The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

本文比较了两种燃气轮机诊断方法的故障识别能力：数据驱动和基于物理的方法（又名气路分析，GPA）。比较考虑了方法之间的两个差异，即诊断空间的类型和诊断决策规则。为此，提出了两个阶段。在第一个方法中，将使用人工神经网络（ANN）识别数据偏差空间中的故障的数据驱动方法与采用相同类型ANN来识别估计故障空间中的故障的混合GPA方法进行比较参数。提出了针对异常检测和故障识别的不同案例研究，以评估诊断空间。它们是通过改变分类，诊断分析的类型和偏差噪声方案形成的。在第二阶段，重建原始GPA，并使用基于公差的规则代替ANN做出诊断决策。这里，正在分析两个方面：GPA分类规则和整体方法的比较。结果表明，对于简单分类，两个空间对于异常检测和故障识别都同样准确。但是，对于复杂的情况，数据驱动的方法可以为故障识别平均提供更好的结果。使用具有ANN的混合GPA进行完全分类，而不是使用基于公差的规则的原始GPA可以使故障识别的识别准确度提高12.49％，对异常检测的识别准确度提高54.39％。至于整个方法的比较，