Several approaches have been employed for gas turbine Fault Detection and Identification (FDI), as a reliable FDI system with minimum false alarm rate can effectively reduce maintenance cost and downtime. This paper introduces the application of Interval Type-2 Fuzzy Logic Systems (IT2FLSs) to gas turbine fault diagnosis for the first time. The proposed FDI system is composed of a bank of IT2FLSs, trained for state detection and health assessment of an industrial gas turbine at various operating conditions. For this purpose, train and test data are generated by applying mechanical fault signatures to gas turbine’s mathematical model. Fuzzy Rule Base is then developed by means of Interval Type-2 Fuzzy C-Means (IT2FCM) clustering, and parameters of the IT2FLSs are optimized using a metaheuristic algorithm. Finally, the performance of the IT2FL based FDI system is compared to several classification techniques. It is concluded that as a compromise among the objectives of online applicability, accuracy, reliability against measurement uncertainty, incipient fault diagnosis, robustness against abrupt sensor failure and generalization capacity, the proposed method demonstrates a promising performance.

燃气轮机故障检测和识别（FDI）已采用多种方法，因为可靠的FDI系统具有最小的误报率，可以有效地减少维护成本和停机时间。本文首次介绍了区间2型模糊逻辑系统（IT2FLSs）在燃气轮机故障诊断中的应用。拟议的FDI系统由一组IT2FLS组成，经过培训可在各种工况下对工业燃气轮机进行状态检测和健康评估。为此，通过将机械故障特征应用于燃气轮机的数学模型来生成训练和测试数据。然后通过区间2型模糊C均值（IT2FCM）聚类来开发模糊规则库，并使用元启发式算法对IT2FLS的参数进行优化。最后，将基于IT2FL的FDI系统的性能与几种分类技术进行了比较。结论是，作为在线适用性，准确性，针对测量不确定性的可靠性，初期故障诊断，针对突然传感器故障的鲁棒性和泛化能力之间的折衷，所提出的方法证明了有希望的性能。