A comparative study on class-imbalanced gas turbine fault diagnosis

Abstract

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.

Civil aviation safety risk intelligent early warning model based on text mining and multi-model fusion

“…Figure 15 shows the confusion matrix 34 of early warning level classification of GBDT, LGBM, XGB, and the Fusion model proposed in this paper. The last value of each row in the confusion matrix represents the recall of such an early warning level, and the last value of each column represents the precision of such an early warning level.…”