Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning DOI Creative Commons
Qinglei Zhang, Longfei Tang,

Jiyun Qin

и другие.

Entropy, Год журнала: 2024, Номер 26(11), С. 956 - 956

Опубликована: Ноя. 6, 2024

Steam turbine blades may crack, break, or suffer other failures due to high temperatures, pressures, and high-speed rotation, which seriously threatens the safety reliability of equipment. The signal characteristics different fault types are slightly different, making it difficult accurately classify faults rotating directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) channel attention mechanism (CAM). 1DCNN can effectively extract local features time series data, while CAM assigns weights each highlight key features. To further enhance efficacy feature extraction classification accuracy, projection head is introduced in this paper systematically map all sample into normalized space, thereby improving model's capacity distinguish between distinct types. Finally, optimization supervised contrastive learning (SCL) strategy, model better capture subtle differences Experimental results show that proposed has an accuracy 99.61%, 97.48%, 96.22% task multiple crack at three speeds, significantly than Multilayer Perceptron (MLP), Residual Network (ResNet), Momentum Contrast (MoCo), Transformer methods.

Язык: Английский

A supervised contrastive learning method based on online complement strategy for long-tailed fine-grained fault diagnosis DOI
Zhiqian Zhao, Yinghou Jiao, Yeyin Xu

и другие.

Advanced Engineering Informatics, Год журнала: 2025, Номер 64, С. 103079 - 103079

Опубликована: Янв. 5, 2025

Язык: Английский

Процитировано

0
Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning DOI Creative Commons
Qinglei Zhang, Longfei Tang,

Jiyun Qin

и другие.

Entropy, Год журнала: 2024, Номер 26(11), С. 956 - 956

Опубликована: Ноя. 6, 2024

Steam turbine blades may crack, break, or suffer other failures due to high temperatures, pressures, and high-speed rotation, which seriously threatens the safety reliability of equipment. The signal characteristics different fault types are slightly different, making it difficult accurately classify faults rotating directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) channel attention mechanism (CAM). 1DCNN can effectively extract local features time series data, while CAM assigns weights each highlight key features. To further enhance efficacy feature extraction classification accuracy, projection head is introduced in this paper systematically map all sample into normalized space, thereby improving model's capacity distinguish between distinct types. Finally, optimization supervised contrastive learning (SCL) strategy, model better capture subtle differences Experimental results show that proposed has an accuracy 99.61%, 97.48%, 96.22% task multiple crack at three speeds, significantly than Multilayer Perceptron (MLP), Residual Network (ResNet), Momentum Contrast (MoCo), Transformer methods.

Язык: Английский

Процитировано

0