Recent advances in data-driven methods have significantly promoted intelligent fault diagnostics for varied industrial applications. However, due to the limitations of machine fault data and the varied scenarios in the context of industrial working conditions, existing diagnostic models can hardly achieve satisfactory results. In this article, we propose a domain discrepancy-guided contrastive feature learning framework for few-shot fault diagnosis under varied working conditions. Unlike the conventional contrastive learning paradigm using manually augmented data, a sample pairs construction is implemented based on the differences between domain distributions for data acquired under different working conditions. The similarity contrast learns the domain-invariant features from a small number of sample pairs. The learned fault features can then be used for fault identification without parameter fine-tuning. In two case studies, we validated the performance of the proposed framework with small training samples under varying speeds, loads, and significant noises. Compared with the state-of-the-art methods, the proposed solution achieved higher diagnostic accuracy for the targeted applications.

The implementation of condition monitoring and fault diagnosis are of special importance for ensuring wind turbine (WT) operation safely and stably. In practice, however, the fault data of WT is limited, which makes it hard to identify faults of WT accurately using existing intelligent diagnosis methods. To address this, signals augmented self-taught learning networks (SASLN) is proposed for the fault diagnosis of the generator, which is one of the most important parts in WT. In SASLN, fault signal samples are generated by the Wasserstein distance guided generative adversarial networks to expand the limited training dataset. The sufficient generated signal samples are used to pre-train the self-taught learning networks (SLN) to enhance the generalization ability of SLN. Then, the weights of SLN are fine-tuned using a small number of real signal samples for accurate fault classification. The effectiveness of SASLN is verified by two bearing vibration datasets. The results show that SASLN can achieve fairly high fault classification accuracy using small training samples. Besides, SASLN has good robustness in the noisy working environment and can also identify faults even in variable loads and variable rotating speeds cases, which makes it meaningful for decreasing the running costs and improving the maintenance management of WT.