Semisupervised Learning for Noise Suppression Using Deep Reinforcement Learning of Contrastive Features

Kazemi, Ehsan; Taherkhani, Fariborz; Wang, Liqiang

doi:10.1109/lsens.2023.3264998

Cited by 3 publications

(1 citation statement)

References 12 publications

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Contrasting

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“…At present, contrastive learning has recently shown its advantages in the field of fault diagnosis due to its ability to learn invariant representations from augmented data [27][28][29]. However, these image-based contrastive learning methods do not work well with time series data, for the following reasons.…”

Section: Introductionmentioning

confidence: 99%

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

Xie,

Tang,

Liu

et al. 2023

Processes

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Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-granularity contrastive learning (MG-CL) framework. First, the original disconnector current data are transformed into two different but related classes: strongly enhanced and weakly enhanced data, by using the strong and weak enhancement modules. Second, we propose the coarse-grained contrastive learning module to preliminarily judge the possibility of faults by learning the features of strongly/weakly enhanced data. Finally, in order to further judge the fault causes, we propose a fine-grained contrastive learning module. By comparing the differences in the data, the final fault type was judged. Our proposed MG-CL framework shows higher accuracy and speed compared with the previous model.

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Section: Introductionmentioning

confidence: 99%