A simulation data-driven semi-supervised framework based on MK-KNN graph and ESSGAT for bearing fault diagnosis.

Current supervised intelligent fault diagnosis relies on abundant labeled data. However, collecting and labeling data are typically both expensive and time-consuming. Fault diagnosis with unlabeled data remains a significant challenge.

Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives.

Intelligent fault diagnosis has been a promising way for condition-based maintenance. However, the small sample problem has limited the application of intelligent fault diagnosis into real industrial manufacturing. Recently, the generative adversarial network (GAN) is considered as a promising way to solve the problem of small sample.

Globally Localized Multisource Domain Adaptation for Cross-Domain Fault Diagnosis With Category Shift.

Deep learning has demonstrated splendid performance in mechanical fault diagnosis on condition that source and target data are identically distributed. In engineering practice, however, the domain shift between source and target domains significantly limits the further application of intelligent algorithms. Despite various transfer techniques proposed, either they focus on single-source domain adaptation (SDA) or they utilize multisource domain globally or locally, which both cannot address the cross-domain diagnosis effectively, especially with category shift.

SDA: Regularization with Cut-Flip and Mix-Normal for machinery fault diagnosis under small dataset.

Data-driven intelligent diagnosis model plays a key role in the monitoring and maintenance of mechanical equipment. However, due to practical limitations, the fault data is difficult to obtain, which makes model training unsatisfactory and results in poor testing performance. Based on the characteristics of 1-D mechanical vibration signal, this paper proposes Supervised Data Augmentation (SDA) as a regularization method to provide more effective training samples, which includes Cut-Flip and Mix-Normal.

Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples.

Rolling bearings are the widely used parts in most of the industrial automation systems. As a result, intelligent fault identification of rolling bearing is important to ensure the stable operation of the industrial automation systems. However, a major problem in intelligent fault identification is that it needs a large number of labeled samples to obtain a well-trained model.