期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 68, 期 9, 页码 8768-8776出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.3013546
关键词
Fault diagnosis; Feature extraction; Three-dimensional displays; Printers; Training; Aerospace electronics; Convolution; Deep learning; fault diagnosis; one-shot learning; three-dimensional (3-D) printer
资金
- GIDTEC Research Group of Universidad Politecnica Salesiana
- National Natural Science Foundation of China [51775112]
- Science and Technology Partnership Program [KY201802006]
- Chongqing Natural Science Foundation [cstc2019jcyj-zdxmX0013]
- CTBU Project [KFJJ2018107, KFJJ2018075]
A one-shot learning-based approach is proposed for multiclass signal classification in fault diagnosis problems, outperforming other methods and achieving good results.
Signal acquisition from mechanical systems working in faulty conditions is normally expensive. As a consequence, supervised learning-based approaches are hardly applicable. To address this problem, a one-shot learning-based approach is proposed for multiclass classification of signals coming from a feature space created only from healthy condition signals and one single sample for each faulty class. First, a transformation mapping between the input signal space and a feature space is learned through a bidirectional generative adversarial network. Next, the identification of different health condition regions in this feature space is carried out by means of a single input signal per fault. The method is applied to three fault diagnosis problems of a three-dimensional printer and outperforms other methods in the literature.
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