Gas turbines are widely used in various fields, and the failure of gas turbines can cause catastrophic consequences. Health condition monitoring and fault diagnosis of gas turbines can detect faults timely, avoid serious faults, and significantly reduce maintenance costs. Thus, fault diagnosis of gas turbines has great significance. Current researches on gas turbine fault diagnosis mainly focus on the case of abundant fault samples. However, fault data are very rare and the number of normal samples is much larger than the number of fault samples in the industrial scene. This class-imbalance problem widely exists but is hardly focused in the field of gas turbine fault diagnosis. Aiming to solve this problem, this paper introduces the concept of class-imbalanced learning from the machine learning field, summarizes three kinds of class-imbalance addressment methods including oversampling, undersampling, and sample weighting, and proposes a new combination method of focal loss and random oversampling for addressing class-imbalance in deep neural networks, and performs a systematic comparative study on class-imbalanced gas turbine fault diagnosis. Experimental results show that class-imbalance can seriously reduce the fault diagnosis accuracy. Through these class-imbalance addressment methods, diagnosis accuracy is greatly improved. Comparative experiments also show that the proposed combination method can obtain the best diagnosis accuracy among all the compared methods in class-imbalanced situation. Through this comparative study, a detailed guideline for improving diagnosis accuracy under class-imbalanced circumstance is provided.

燃气轮机广泛应用于各个领域，燃气轮机的故障会造成灾难性的后果。燃气轮机的健康状态监测和故障诊断可以及时发现故障，避免严重故障，显着降低维护成本。因此，燃气轮机的故障诊断具有重要意义。目前对燃气轮机故障诊断的研究主要集中在故障样本丰富的情况下。但是，故障数据非常少，正常样本的数量远大于工业场景中的故障样本数量。这种类不平衡问题广泛存在，但很少集中在燃气轮机故障诊断领域。针对这个问题，本文从机器学习领域引入了类不平衡学习的概念，总结了过采样、欠采样和样本加权三种类不平衡处理方法，提出了一种新的focal loss和随机过采样相结合的方法来解决深度神经网络中的类不平衡问题，并对类不平衡问题进行了系统的比较研究燃气轮机故障诊断。实验结果表明，类不平衡会严重降低故障诊断的准确率。通过这些类不平衡处理方法，大大提高了诊断的准确性。对比实验还表明，在类不平衡情况下，所提出的组合方法在所有对比方法中可以获得最好的诊断准确率。通过这项比较研究，为提高类别不平衡情况下的诊断准确性提供了详细的指导方针。