Fault diagnosis is a crucial area of research in industry. Industrial processes exhibit diverse operating conditions, where data often have non-Gaussian, multi-mode, and center-drift characteristics. Data-driven approaches are currently the main focus in the field, but continuous fault classification and parameter updates of fault classifiers pose challenges for multiple operating modes and real-time settings. Thus, a pressing issue is to achieve real-time multi-mode fault diagnosis in industrial systems. In this paper, a novel approach to achieve real-time multi-mode fault diagnosis is proposed for industrial applications, which addresses this critical research problem. Our approach uses an extended evidence reasoning (ER) algorithm to fuse information and merge outputs from different base classifiers. These base classifiers based on broad learning system (BLS) are trained to ensure maximum fault diagnosis accuracy. Furthermore, pseudo-label learning is used to update model parameters in real-time. The effectiveness of the proposed approach is demonstrated on the multi-mode Tennessee Eastman process dataset.

翻译：故障诊断是工业领域的重要研究方向。工业过程具有多样化的运行工况，数据往往呈现非高斯性、多模态性和中心漂移特征。当前数据驱动方法虽是该领域的研究重点，但连续故障分类与分类器参数更新给多工况和实时场景带来挑战。因此，实现工业系统实时多模态故障诊断成为亟待解决的问题。本文针对这一关键研究问题，提出了一种面向工业应用的新型实时多模态故障诊断方法。该方法采用扩展证据推理算法融合不同基分类器的输出信息，这些基于宽度学习系统训练的基分类器能够确保故障诊断精度最大化。此外，通过伪标签学习实现模型参数的实时更新。在多模态田纳西伊士曼过程数据集上的实验验证了该方法有效性。