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Many deep learning methods typically assume that the marginal probability distribution between the training and testing bearing data is similar or the same. However, the probability distribution of rolling bearings may deviate significantly under diverse working conditions. To address the above limitations, a novel transferable remaining useful life (RUL) prediction method integrated with Bayesian deep learning and unsupervised domain adaptation (DA) is proposed. First, the signal alignment is executed on the data after the first prediction time to maintain the same granularity and scale across both source and target domains. Second, the multi‐domain features are extracted and sent into the dual‐channel Transformer network (DCTN) incorporating the convolutional block attention module (CBAM) to adequately exploit the abundant degradation information. Then, the DA module is incorporated into the model to mitigate the distribution discrepancies of the extracted high‐level merged features between the source and target domains. Finally, by applying the variational inference method, the DCTN‐CBAM is extended to the Bayesian deep neural network, and the RUL prediction and its corresponding confidence intervals can be conveniently derived. In addition, the generalization capability and effectiveness are validated through six bidirectional transfer RUL prediction tasks across two rolling bearing datasets. The experimental results demonstrate that it could provide a more reliable RUL prediction and efficiently account for the prediction uncertainty. [ABSTRACT FROM AUTHOR]

This paper introduces a novel parallel deep neural network for fault diagnosis of drilling pumps. It integrates the Convolutional Block Attention Module with the AlexNet and synchronizes with the Anomaly Transformer model to delve meticulously into both the time and time-frequency domains of signals. The method prioritizes the singular extraction and subsequent amalgamation of features, facilitating a detailed view of diagnostic data and mitigating the risk of interference and overfitting. The integration of the anomaly attention of the Anomaly Transformer with the features of the Convolutional Block Attention Module results in a distinctive dual attention mechanism that is critical to the methodology. This mechanism emphasizes essential features in both the time domain and the time-frequency domain, improving the accuracy of fault diagnosis. Verification with on-site data underscores the preeminence of the approach over existing models, signaling improved reliability and accuracy in diagnosing faults in drilling pumps. This meticulous approach offers promising advances in the study and application of fault diagnosis in energy equipment, demonstrating increased efficiency and refined accuracy. • A novel Parallel Deep Neural Network-Based Fault Diagnosis Framework is proposed. • Time and time-frequency domains of signals are extracted and fused for Fault Diagnosis. • Integrating the Transformer with CBAM to consider the essential features of signals. [ABSTRACT FROM AUTHOR]