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Semi-supervised deep architecture for classification in streaming data with emerging new classes: application in condition monitoring
  • Yu Wang ,
  • Qingbo Wang ,
  • Alexey Vinogradov
Qingbo Wang
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Alexey Vinogradov
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Classification in streaming data with emerging new classes (SENC) is challenging because the surrogate models trained on the closed datasets are open to new information never seen before. The fundamental sub-tasks include: (1) Pattern recognition of these already known classes with high accuracy; (2) Timely identification of emerging new classes; (3) Model update to adapt to new classes and (4) distinguish different new classes.  Existing SENC frameworks utilize predefined threshold to set the ambiguous decision boundaries between the known and emerging new classes, they have poor performance in distinguishing different emerging new classes. To this end, a novel SENC framework leveraging Generative Adversarial Network (GAN) and history-state ensemble method is proposed in this paper, which is referred to as ensemble multi-classification GAN (EMC-GAN). Compared with traditional SENC frameworks, the proposed method is designed based on deep learning, thus, it can automatically explore implicit features from the raw data without human intervention. Besides, it doed not require pre-set threshold for novelty detection. The proposed method is evaluated on the condition monitoring of rotating machinery - a typical SENC case in real-world industry. Experimental results show that the proposed SENC framework can not only effectively detect emerging new classes from known ones, but also exhibit excellent performance in distinguishing different new classes.