With the rapid development of railroads and the yearly increase in the scale of operation, the safe operation and maintenance of rail trains have become particularly important. Among them, deep learning-based bearing fault diagnosis methods have attracted more and more attention in rail train operation and maintenance. However, rail trains usually operate normally. Collecting complete fault data for deep learning model training is often difficult. Such scenarios with a large difference between the number of normal data and fault data usually affect the performance of fault diagnosis models. Here, an interactive generative feature space oversampling-based autoencoder (IGFSO-AE) is proposed to realize fault sample generation under imbalanced data. First, the original vibration signal is converted into a semantically stable amplitude–frequency signal by fast Fourier transform and input into the autoencoder; second, the order of the hidden layer space features of the autoencoder is randomly exchanged, and the interactive sample generation learning strategy trains the autoencoder; then, interpolation oversampling is used to interpolate samples in the hidden layer space where the Euclidean distance between samples is large, and is input into the decoder, the generated samples are mixed with the original samples to form a new training set, which is used to train the intelligent fault diagnosis model and output the diagnosis results. Finally, the performance of the proposed method is evaluated using the publicly available bearing dataset and the bogie-bearing fault simulation bench in our lab. The experimental results show that IGFSO-AE can generate diverse samples with incremental information and exhibits robustness and superiority in different imbalanced proportions of data.

For data-driven remaining useful life (RUL) prediction of rolling bearing (RB), deep learning methods usually do not consider the difference in distribution due to different operating conditions, which adversely affects the prediction results. Recently, transfer learning has been a research hotspot, and it can mitigate the above problem effectively. However, for different bearings under the same working condition, the data distribution changes due to the location and time of failure. To solve these problems, a transfer prediction model based on the dynamic benchmark (DB) has been proposed to predict the RUL of bearings in this study. The method is divided into three processes. First, a fully convolutional variational autoencoder is used to perform unsupervised learning; next, a domain adaptation method based on the DB is employed for reconstruction; finally, extracted hidden layer features are input to the fully connected layer of the proposed model to obtain the final prediction result. The prediction and health management (PHM) Challenging 2012 and XJTU-SY RB accelerated life test datasets were used for verification. The results showed that the proposed method could significantly improve prediction accuracy and had good stability and generalizability.

In recent years, supervised deep learning methods have proven to be feasible and advanced for fault diagnosis. They do, however, rely on large amounts of accurately labeled data. It is challenging to accurately label incipient or secondary fault signals in the presence of operational background noise, resulting in noisy labels. This study presents a bidirectional guidance method that enables deep networks to diagnose faults robustly with the ability to label noise tolerance. First, the proposed method uses discrete wavelet packet transform to preprocess vibration signals before feeding them into the deep convolutional network. Second, a bidirectional loss function is proposed to significantly improve the robustness of the model in the case of noisy labels. Third, a fast cosine decay strategy of the learning rate is designed to further boost the recognition performance of the model under severe noisy labels. The proposed method is applied to testbed experiments and wind turbines fault diagnosis. The experimental results demonstrate that it outperforms other cutting-edge methods in fault diagnosis with severely noisy labels.

Accurate prediction of the remaining useful life (RUL) of rolling bearings can effectively ensure the safety of complicated machinery and equipment in service. However, the diversity of rolling bearing degradation processes makes it difficult for deep learning-based RUL prediction methods to improve prediction accuracy further and provide generalizability for engineering applications. This study proposed a novelty RUL prediction model for rolling bearings based on a bi-channel hierarchical vision transformer to reduce the impact of the above problems on prediction accuracy improvement. Firstly, hierarchical vision transformer network structures based on different-sized patches were employed to extract depth features containing more degradation processes information from input samples. Second, the dual channel fusion method is implemented into classic RUL prediction networks based on a multi-layer fully connected network to improve prediction accuracy. With two distinct validation experimental arrangements utilizing the datasets from PHM 2012, the prediction accuracy of the proposed approach can be increased by up to 9.43% and 43.10%, respectively, compared with the current standard method. The results demonstrate that the proposed method is more suitable for rolling bearing RUL prediction.

We demonstrate a chip-based octave-spanning soliton microcomb in a whispering gallery mode microresonator platform. By fabricating a silica microdisk resonator and optimizing its dispersion with dry etching, we achieve an octave-spanning single-soliton microcomb with a repetition rate of ∼670 GHz at an optical pump power of 162.6 mW. Also, two dispersive waves at the end of the spectrum are observed to extend the comb spectral range and improve the comb power.

The transfer learning (TL) method represented by domain adaptation (DA) can effectively improve the prediction accuracy of rolling bearings’ remaining useful life (RUL) under different working conditions. However, the difference in the bearing degradation process under the same working conditions limits the reliability and generalization of the transfer RUL prediction model. Owing to the aforementioned problems, this study proposed an RUL transfer predicting method for rolling bearings based on working conditions’ common benchmark. An attention mechanism autoencoder (AMAE) is proposed to extract the common benchmark under each working condition and improve the commonness between deep features. The dynamic benchmark constraint (DBC) under the same working conditions was proposed to ensure the reliability of a common benchmark and improve the prediction accuracy in the process of benchmark transfer under different working conditions. Verified by Xi’an Jiaotong University (XJTU) and the Changxing Sumyoung Technology Co., Ltd. (SY) bearing datasets, the proposed method can effectively obtain a common benchmark that can be used for DA under various working conditions. In the experimental design of six sets of RUL prediction tests under different working conditions, more than 50% of the experimental tasks have better prediction results using the proposed method. The proposed approach increases the overall prediction accuracy to 11.74% compared with the method without DA. Experimental results show that the proposed method can better meet the needs of intelligent operation and maintenance in practical engineering