Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise

Unsupervised domain adaptation for intelligent fault diagnosis requires a well-annotated source domain to transfer knowledge to an unlabeled target domain, but the ubiquitous source label noise in realistic scenarios remains largely neglected. Recent efforts following adversarial domain adaptation attempt to learn with label noise conditioned on the classifier predictions. However, an essential flaw in the classifier capacity introduces improper adjustments to the loss function. Moreover, they treat domain-specific and domain-invariant representations as a whole, which threats the effectiveness of learning invariant representations. To address these issues, a Convolutional Kernel Aggregated Domain Adaptation (CKADA) strategy is proposed for fault knowledge transfer. Specifically, a convolutional kernel aggregated layer with domain-mixed attention weights is first designed to harness the diverse learning capacities of multiple kernels. Then, by extending such a layer to the classifier, a classification bridge layer is presented to ensure reliable predictions, based on which the side effects of label noise are further relieved through selecting and reusing source samples. Meanwhile, an additional discrimination bridge layer is constructed, which collaborates with the classification bridge layer to assist adversarial domain adaptation. Extensive experiments on three rolling bearing datasets with various types of noisy transfer tasks demonstrate the effectiveness and robustness of CKADA.