Vibration Sample Augmentation Method Based on the TCV_CGAN Model for Turbine Blade Damage Detection

The fault detection of steam turbine blades faces severe engineering challenges, such as insufficient fault data, imbalanced class distribution, and difficulty in extracting weak damage signal features, which seriously restrict the accuracy and generalization performance of intelligent diagnostic models. To solve the bottleneck problem of small samples mentioned above, a new time convolutional vector conditional generative adversarial network (TCV_CGAN) is proposed for data augmentation and damage detection of turbine blade. Firstly, multi-dimensional vector conditional encoding and stacked extended causal time convolution modules are integrated into an adversarial generation framework, and a composite loss function is constructed by combining adversarial loss and conditional matching loss. Based on the proposed TCV_CGAN, the controllable high fidelity vibration sample generation is obtained corresponding to different types of damage, degrees of damage, and rotational speed working conditions. In addition, the turbine blade damage detection framework is established based on the balanced augmented dataset generated from the TCV_CGAN, and it can improve the accuracy and reliability of damage detection for turbine blade. Moreover, the performance of the proposed method is analyzed and tested on an experimental turbine blade platform on sample augmentation and damage detection. The results indicate that the samples generated by TCV_CGAN maintained a high degree of distribution similarity and structural consistency with real vibration data with below 0.65 maximum mean difference and over 0.82 structure similarity index measure. In addition, the enhanced dataset significantly improved the effectiveness of blade damage detection at different speeds with the precision over 90% under all working conditions. The effectiveness and superiority of the proposed method are further verified through comparative analysis, which provides an effective data augmentation solution for fault diagnosis of small sample turbine blades.