Multi-source domain adaptation (MSDA) has demonstrated superior performance in intelligent fault diagnosis (IFD) compared to single-source domain adaptation (SSDA), as it can provide more comprehensive and diverse information from multiple fully-labeled source domains. However, in many real industrial scenarios, acquiring multiple fully-labeled source domains is challenging because labeling all the source domains is as expensive and laborious as labeling the target domain. Given this concern, a cross-supervised multisource prototypical network (CSMPN) is proposed for multi-source few-shot fault diagnosis. Specifically, a domain-shared and a domain-individual branch are constructed to realize shared domain alignment across all the source and target domains and individual domain alignment of source-target domain pairs, respectively. Within two branches, domain alignment is realized by the designed prototypical contrastive learning (PCL) module. In the PCL module, we propose a prototype calibration strategy to address the issue of biased prototype estimation owing to outlier samples. In addition, a two-stage pseudo-labeled sample selection mechanism is proposed to enhance the feature representation ability of two branches. At the end of the two branches, we design a cross-supervised learning (CSL) module to realize mutual and collaborative learning between the two branches, which can further improve the diagnosis performance on the target domain. Experiments on two different bearing datasets are implemented to verify the superiority of the proposed method compared with the comparison methods. Our code is available at https://github.com/YNWA-Zhang/CSMPN .

Machine learning models have been widely successful in the field of intelligent fault diagnosis. Most of the existing machine learning models are deployed in static environments and rely on precollected datasets for offline training, which makes it impossible to update the models further once they are established. However, in the open and dynamic environment in reality, there is always incoming data in the form of streams, including new categories of data that are constantly generated over time. In addition, the operating conditions of mechanical equipment are time-varying, which results in continuous stream data that are nonindependently and homogeneously distributed. In industrial applications, the diagnosis problem of nonindependent and identically distributed continuous streaming data is referred to as the cross-domain class incremental diagnosis problem. To address the cross-domain class incremental problem, a novel cross-domain class incremental broad network (CDCIBN) is proposed. Specifically, to solve the nonindependent identically distributed problem, a novel domain-adaptation learning loss function is first designed, which enables the conventional broad network to handle the category increment task well. Then, a cross-domain class incremental learning mechanism is designed, which learns new categories while retaining the knowledge of old categories well enough without replaying old category data. The effectiveness of the proposed method is evaluated through multiple mechanical failure increment cases. Experimental analysis demonstrates that the designed CDCIBN has significant advantages in the variable working condition class incremental application.

Domain adaptation technologies have been extensively explored and successfully applied to machine fault diagnosis, aiming to address problems that target data are unlabeled and have a certain distribution bias with source data. Nonetheless, existing fault diagnosis methods mainly explore feature-level alignment strategies to reduce domain discrepancies, which not only fails to directly ascertain the relationship between the target output and domain deviation, but also cannot guarantee accurate diagnosis results (i.e., learning class-discriminative features) when only relying on feature adaptation. In light of these issues, a more intuitive and effective domain adaptation method is developed for intelligent diagnosis of machinery in this article, in which the minimum class confusion and maximum nuclear norm-based target prediction constraints are simultaneously designed to promote learning reliable domain-invariant and discriminative features for accurate fault diagnosis. We conduct extensive experiments based on two different mechanical systems to evaluate the proposed method. Comprehensive results and discussions demonstrate the promising performance of our approach.

Deep networks based mechanical intelligent diagnosis has been recently attracting considerable attentions with the development of Industry 4.0. Unfortunately, a more practical diagnostic scenario, i.e., unsupervised partial transfer diagnosis, has not yet been well addressed. In view of this, a novel unsupervised intelligent diagnosis framework named classifier inconsistency-based domain adaptation network is proposed in this article. In this approach, two discriminative one-dimensional convolutional networks are designed as the basic architecture. The source samples of the same categories as the target domain are then identified and emphasized to boost positive network training. Meanwhile, the classifier inconsistency is introduced to guide the model to learn discriminative and domain-invariant representations for the correct classification of unlabeled target data. Extensive experiments on two datasets are conducted to evaluate the proposed method. Additionally, five popular methods are selected for comparison. The comprehensive results validate the effectiveness and superiority of the proposed approach.