Auxiliary Information-Guided Industrial Data Augmentation for Any-Shot Fault Learning and Diagnosis
Yue Zhuo, Zhiqiang Ge
Abstract
The label scarcity problem widely exists in industrial processes. In particular, samples of some fault types are extremely rare; even worse, the samples of certain faults cannot be accessed, but they may appear in the actual process. These two kinds of challenges together can be termed as any-shot learning problem in industrial fault diagnosis. In this article, taking the advantages of generative adversarial network, a generative approach is proposed to tackle the any-shot learning problem, which generates the abundant samples for those rare and inaccessible faults, and trains a strong diagnosis model. To reach this, an attribute space is built to introduce the auxiliary information, which achieves the diagnosis of unseen faults and makes the generated samples more resembled to the real data. Besides, an auxiliary loss of triplet form is introduced as a joint training loss term, further improving the quality of augmented data and diagnosis accuracy. Finally, the performance of model is verified by the experiments of a hydraulic system and Tennessee-Eastman process, the results of which show that our method performs excellently for both zero-shot and few-shot fault diagnosis problems.