The rolling bearing is the key component of rotating machinery, and it is also a failure–prone component. The intelligent fault diagnosis method has been widely used to accurately diagnose bearing faults. However, in engineering practice, it is difficult to obtain sufficient sample data to train the intelligent diagnosis model. Therefore, in this article, a fusion diagnosis model CGAN-2-D-CNN that combines a conditional generative adversarial network (CGAN) and a two-dimensional convolutional neural network (2-D-CNN) is proposed for bearing fault diagnosis with small samples. Considering the problem of insufficient sample data, CGAN is used to learn the data distribution of real samples to generate new samples with similar data distribution by the confrontation training of the generator and discriminator. Then, 2-D preprocessing is conducted to convert the generated 1-D data into 2-D gray images. Finally, these images are input into the 2-D-CNN to extract the features and classify the bearing fault types. Two experimental cases are implemented to validate the effectiveness and feasibility of the proposed CGAN-2-D-CNN. The experimental results illustrate that the diagnosis accuracy of the proposed method used on the small sample data is close to that of the 2-D-CNN directly used on the enough original sample data whose size is equal to the expanded sample size. In addition, compared with the 1-D-CNN, support vector machine (SVM), and long short-term memory (LSTM) models, the 2-D-CNN model after 2-D preprocessing has the higher fault classification accuracy.

To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL.

Because of uncertain working conditions and the lack of labels for most conditions, transfer learning has great potential in fault diagnosis for high-speed train bogies. This paper proposed a novel domain adaptation model for intelligent diagnosis under varying working conditions, namely Deep Adversarial Hybrid Domain-Adaptation Network (DAHAN). In the proposed method, to further improve the transfer learning performance at a low signal-to-noise ratio, the local domain adaptation by Wasserstein distance is developed to align category-level features which are combined with global domain adaptation to hybrid domain adaptation. In addition, an optimized training procedure is constructed to improve the stability and training speed of the proposed adversarial domain adaptation model to enhance the efficiency and real-time performance of the diagnosis method. The effectiveness and advantage of DAHAN are verified by the CRWU bearing data set. Finally, trained by train bogie gearbox fault dataset, the proposed method shows a great performance improvement and avoids the mismatch of weak fault features, proving its applicability on train bogie fault diagnosis.

The effects of oxidative aging on the static and dynamic properties of nitrile rubber at the molecular scale were investigated by molecular dynamics simulation. The aged nitrile rubber models were constructed by introducing hydroxyl groups and carbonyl groups into rubber molecular chains to mimic oxidative aging. The static and dynamic properties of the unaged and aged nitrile rubber under different conditions were evaluated by mean square displacement, self-diffusion coefficients, hydrogen bond, fractional free volume, radial distribution function, cohesive energy density and solubility parameter. The results show that the elevated temperature intensified significantly the mobility of rubber molecular chains and fractional free volume, while the compressive strain displayed the opposite effect resulting in packing and rearrangement of rubber chains. The introduction of hydroxyl groups and carbonyl groups enhanced the polarity, intermolecular interactions, the volume and rigidity of molecular chains, implying weaker mobility of molecular chains as compared to unaged models. The compressive strain and oxidative aging both decreased the fractional free volume, which inhibited gaseous and liquid diffusion into the rubber materials, and slowed down the oxidative aging rate. This study provides insights to better understand the effect of molecular changes due to oxidative aging on the structural and dynamic properties of rubber materials at the molecular level.