Corrigendum to “Aero-engine high speed bearing fault diagnosis for data imbalance: A sample enhanced diagnostic method based on pre-training WGAN-GP” [Measurement 213 (2023) 112709]

Rolling bearing is the key supporting component of aero-engines, of which fault diagnosis is very important ensure its reliable operation and continuous airworthiness. However, the data imbalance problem caused by complex and harsh environment restricts the intelligent diagnosis. This paper proposes a sample enhanced diagnostic method based on pre-training and auxiliary classifier Wasserstein generative adversarial network with gradient penalty (PT-WGAN-GP). Firstly, a pre-training network is proposed and incorporated into discriminator and classifier of WGAN-GP for feature adaptive and efficient extraction. Meanwhile, a new generator is constructed by introducing a residual network and the instance batch to improve its data-fitting ability. Finally, the data-enhanced model, PT-WGAN-GP, can stably generate high-quality faulty samples, which balances the testing dataset and completes the optimization training of network structure. Two cases under imbalanced data have verified the effectiveness of the proposed method, as well as its superiority over other widely used methods.