STP-Model: A semi-supervised framework with self-supervised learning capabilities for downhole fault diagnosis in sucker rod pumping systems

Author:

Huang Zongchao,Li Kewen,Xu Zhifeng,Yin Ruonan,Yang Zhixuan,Mei Wang,Bing Shaoqiang

Funder

National Natural Science Foundation of China

Shandong Province Natural Science Foundation

Publisher

Elsevier BV

Reference56 articles.

1. Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study;Azar;Reliab. Eng. Syst. Saf.,2022

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3. A deep learning framework for time series classification using relative position matrix and convolutional neural network;Chen;Neurocomputing,2019

4. Intelligent fault diagnosis with noisy labels via semisupervised learning on industrial time series;Cheng;IEEE Trans. Ind. Inform.,2023

5. Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor;Choudhary;Eng. Appl. Artif. Intell.,2023

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