Intelligent fault diagnosis scheme for rotating machinery based on momentum contrastive bi-tuning framework

被引:13
|
作者
Zhong, Jiankang [1 ]
Mao, Hanling [1 ,2 ]
Tang, Weili [1 ]
Qin, Aisong [1 ]
Sun, Kuangchi [1 ,3 ]
机构
[1] Guangxi Univ, Sch Mech Engn, Nanning 530004, Peoples R China
[2] Guangxi Key Lab Mfg Syst & Adv Mfg Technol, Nanning 530004, Peoples R China
[3] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China
关键词
Intelligent fault diagnosis; Deep transfer learning; Rotating machinery; Contrastive learning; Gramian Angular Fields; BEARING;
D O I
10.1016/j.engappai.2023.106100
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Existing fine-tuning methods mainly leverage the discriminative knowledge and discard the intrinsic structure of data. In this paper, we propose a novel framework Momentum Contrastive Bi-Tuning (MCBiT) for intelligent diagnosis of rotating machinery, which can fully exploit both the discriminative knowledge of labels and the in-trinsic structure of target data in a boosting fine-tuning way. One-dimensional vibration signals are transformed by Gramian Angular Difference Field (GADF) and fed into MCBiT, which enhances the conventional fine-tuning by integrating two branches on the ImageNet-pretrained backbone: a classifier with an instance-contrastive cross-entropy loss to better exploit label knowledge; and a projector with a categorical contrastive learning loss to mining the intrinsic structure of data. Our proposed approach outperforms state-of-the-art methods on six publicly available rotating machinery fault diagnosis datasets and our experimental-collected dataset at different data scales. The promising performance of our proposed MCBiT contributes toward more practical data-driven approaches that can realize timely deployment under challenging real-world environments.
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页数:13
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