A Domain Feature Progressive Fusion Network for Fault Diagnosis of Rotating Machinery under Noisy and Limited Sample Conditions

Fault diagnosis in rotating machinery is crucial for ensuring the safety and efficient operation of industrial manufacturing processes. However, in practical applications, fault samples are typically noisy and limited in quantity, which reduces the performance of diagnostic models. To address this, this paper proposes a domain feature progressive fusion network. The network effectively integrates both time-domain and frequency-domain information, and through a progressive fusion approach, repeatedly combines time-domain and frequency-domain features, thereby significantly enhancing its capacity for feature extraction, learning, and transfer. Specifically, a multi-scale feature extraction module is proposed, aimed at thoroughly uncovering the multi-scale and multi-level latent features within both time-domain and frequency-domain samples. Additionally, a multi-scale cross-perception attention mechanism is proposed to enhance the representational power of key features within both time-domain and frequency-domain data. Moreover, a multi-classifier structure and a joint optimization strategy are proposed, further advancing the learning and transfer of key features. Experimental results demonstrate that, on two datasets, the proposed method achieved average diagnostic accuracies of 97.75% and 98.34%, respectively, outperforming the comparative methods.