An Industrial Fault Sample Reconstruction and Generation Method Under Limited Samples With Missing Information
针对数据驱动故障诊断中信息缺失且样本有限的问题，提出联合样本重构与生成的方法，通过差异化重构和自适应融合提升生成样本质量，实验验证了有效性。
The problem of limited samples with missing information is an open challenge in data-driven fault diagnosis. Existing work has limited application in this field, since the reconstructed missing samples participating in sample generation may hurt the quality of the generated samples. To address this issue, the joint modeling of sample reconstruction and sample generation is proposed. First, the differentiated evaluation and reconstruction strategies are designed, which make reconstructed samples more reasonable and realistic, so that they can be employed to participate in sample generation. Second, the adaptive fusion mechanism is presented to introduce the knowledge of actual fault samples into the laboratory simulation samples, by which the quality and diversity of generated samples are guaranteed. By doing so, limited samples with missing information are enhanced to enable reliable fault diagnosis modeling. The proposed method is applied to the actual industrial process and benchmark simulated process. The experimental results highlight the superiority of the proposed method.