Intelligent Working Conditions Recognition Method and Application Incorporating Semi-Supervised Learning With Multilevel Information Fusion

Real time and precise recognition of industrial process working conditions is essential for control and optimization, playing a crucial role in ensuring production safety and efficiency. Nevertheless, the substantial costs associated with labeled samples result in the underutilization of unlabeled data in production. Additionally, leveraging the complementary characteristics of heterogeneous data can enhance recognition accuracy. Therefore, this article proposes a novel intelligent working conditions recognition method that integrates semi-supervised learning and multilevel information fusion, effectively increasing information capacity and enhancing modeling accuracy. Initially, a multiscale feature fusion network (MSFFNet) is established to accommodate the fine-grained feature requirements of image modeling. Subsequently, samples are evaluated for uncertainty both before and after semi-supervised recognition, using an aggregation strategy to refine the quality of pseudolabels. Ultimately, the features of heterogeneous data are fused using a bilinear attention network, and a consistent description of working conditions is achieved through decision-level fusion, culminating in the final working conditions recognition results. The effectiveness of the proposed method is validated with actual industrial multimodal data, achieving a remarkable recognition accuracy of 98.91%.