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As a popular renewable energy generation technology, wind turbine system has become a critical enabler for building the sustainable cyber‐physical system (CPS). The main shaft bearing is an important part of the wind turbine CPS and often runs under variable working conditions. Thus, the reliable bearing diagnosis method can timely discover the main shaft bearing fault, which reduces the maintenance cost of wind turbines. Inspired by the idea of domain adaptation, we combined domain adversarial neural network and residual network and proposed a novel deep domain adversarial residual neural network (DDA‐RNN) for diagnosing bearing fault and improving model performance on the unlabeled dataset. This proposed software and hardware co‐design method was evaluated by our bearing dataset, which was collected from two wind turbine CPSs from Sanmenxia in Henan Province. Besides, F1 score and accuracy are served as model metrics, which reflect the diagnosis performance. Compared with other methods, the experimental results show that DDA‐RNN can improve model performance. Meanwhile, DDA‐RNN extracts diagnosis knowledge from labeled dataset and improves the model performance on the unlabeled dataset under different working condition. Therefore, the proposed method can be potentially used to benefit many practical scenarios in the future.

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Three kinds of electrode with different niobium contents were developed and compared, and the influence of alloying elements on microstructure and mechanical properties was summarized. Strong carbide elements such as Nb, Ti, and V were added to form stable precipitates. The existence of intercrystalline precipitates leads to pinning and zigzag of grain boundaries, hinders the propagation of cracks, and enhances the low temperature strength and impact toughness of the materials. The No. 1 and No. 2 ENiCrMo-6 electrodes meet the requirements of LNG (liquified natural gas) equipment. The tensile strength of the deposited metal reaches 687 MPa, while the average impact energy at −196°C is 131 J. Owing to fluctuations in the stress concentration at the junction of grain boundaries, cracks may easily form. MC carbide can retard the crack propagation. With the increase of Nb and other alloys, the strength and hardness increase gradually, but the plasticity and toughness are retarded to a certain extent.