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Intelligent fault diagnosis scheme for rotating machinery based on momentum contrastive bi-tuning framework.
- Source :
-
Engineering Applications of Artificial Intelligence . Jun2023, Vol. 122, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- 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 intrinsic 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ROTATING machinery
*FAULT diagnosis
*BOOSTING algorithms
*DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 122
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 163869926
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.106100