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In recent years, deep learning methods, especially convolutional neural network (CNN), have received extensive attentions and applications in fault diagnosis. However, recent studies have shown that the shift-invariance of CNN is not good enough, resulting in fragile feature extraction and sharp reduction in model performance when the shift occurs in the input. To improve the shift-invariance of CNN, considering the periodic characteristics of vibration signals, a multiscale CNN with feature alignment (MSCNN-FA) is proposed for bearing fault diagnosis under different working conditions. First, by analyzing the operating principles of the convolutional layer and pooling layer, a feature alignment module including single-stride convolution layer, adaptive max-pooling layer, and global average pooling layer is designed to obtain aligned features. Next, to extract shift-invariant robust features from vibration signals, a multiscale convolution strategy is utilized, and a feature-aligned multiscale feature extractor is constructed. Finally, a classifier composed of fully connected (FC) layers is constructed for bearing fault diagnosis. The effectiveness of the method is verified by a rolling bearing experiment, which outperforms other related existing CNN-based methods in terms of diagnosis accuracy and feature robustness. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many challenges such as complex degradation processes, varying working conditions, and insufficient run-to-failure data. Transfer learning (TL), one paradigm of artificial intelligence technology, has demonstrated its powerful performance and great effectiveness for such challenges. As a result, many TL-based solutions have been widely developed and extensively studied for rolling bearing RUL prediction. Admittedly, several review articles have been published on RUL prediction. Nevertheless, the majority of these articles only concentrated on deep learning-based RUL prediction methods, and a review article that systematically overviews the status of TL-based RUL prediction has not been published. Therefore, it is urgent and significant to thoroughly summarize the academic publications and industrial applications related to TL-based RUL prediction, and present its potential challenges and future research directions. With such goals, the problem definitions of TL-based RUL prediction, the general procedure of RUL prediction, and typical TL-based RUL prediction algorithms are first introduced to help researchers quickly overview the state-of-the-art approaches and recent developments. Thereafter, relevant TL-based RUL prediction solutions are comprehensively discussed from the perspectives of three industrial scenarios, providing suggestions to researchers and engineers for selecting appropriate solutions in practical industrial applications. Finally, the key challenges and future trends in RUL prediction are presented to conclude this paper. We hope that this review of TL-based RUL prediction for rolling bearings can contribute to a better understanding of intelligent prognostic technology and will inspire researchers to extend their work on RUL prediction. [ABSTRACT FROM AUTHOR]

• A generalized open-set fault diagnosis (OSFD) scenario is defined. • A multiple metric weighting learning network is constructed to address two OSFD issues simultaneously. • Enhanced domain similarity measurement is designed to leverage class label information. • Ensemble uncertainty measurement is developed to reduce misclassification interference. • Twenty-seven OSFD diagnosis tasks are used to evaluate the proposed approach. The problem of practical open-set domain adaptation diagnosis has gained great attention considering unobserved fault categories in target domain. However, existing studies assume that the label space of source domain is a subset of target domain, ignoring that source domain may also contain private fault categories. This generalized open-set diagnosis issue is more challenging, making existing techniques less effective. To tackle this problem, a novel approach is proposed that focuses on addressing two open-set diagnosis issues simultaneously. A multiple metric weighting learning strategy with the integration of an enhanced domain similarity measurement and an ensemble uncertainty measurement is constructed to adaptively weight the importance of samples across domains. Then, weighted adversarial training with multiple metric weight functions is implemented to learn domain-invariant features by performing alignment across different distributions. As such, both unknown and known fault categories can be simultaneously and effectively recognized. Experiments on three bearing datasets are carried out. Results demonstrate the proposed approach can effectively deal with generalized open-set diagnosis tasks, outperforming existing diagnosis approaches. [ABSTRACT FROM AUTHOR]