A Domain Adaptation Meta Learning Method With Multilayer Convolutional Attention for Cross-Domain Bearing Fault Diagnosis

Abstract

In industrial applications, intelligent fault diagnosis technology is rapidly evolving, with deep learning-based intelligent diagnosis methods proving effective in managing and maintaining equipment. However, the variability of working conditions for mechanical equipment in actual industrial settings often presents challenges. The data collected by sensors from different working conditions or machines may exhibit significant differences in distribution. In addition, it is difficult to collect a large number of labeled samples. This article introduces a domain-adaptive meta-learning method with multilayer convolution attention, named (MCA-DAML), designed for cross-domain bearing fault diagnosis. The proposed approach involves taking the source domain data and unlabeled target domain data as inputs. Through adversarial domain adaptation (DA), the model is trained to simultaneously minimize the source domain task loss and maximize the confusion error of the domain discriminator. This dual optimization strategy encourages the model to learn shared feature representations that are effective across different domains. Multilayer convolutional attention modules are used to enhance the feature extraction capabilities of the model and suppress redundant features, which are analyzed by the prototype network for proximity to established fault prototypes, ultimately achieving accurate fault classification. Evaluated using three bearing vibration datasets without labeling the target domain samples, the average accuracy achieved was 99.86%, 96.51%, and 94.24% when performing the three cross-domain cases within the same machine and between different machines, respectively. The experimental results validate the superior performance of our method relative to other diagnostic methods.