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RISS 인기검색어
- 검색키워드
- 저자 : Yaofeng Liu (검색결과 3 건)
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Bin Liu,Changfeng Yan,Zonggang Wang,Yaofeng Liu,Lixiao Wu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7
Deep learning is widely used in the field of rolling bearing fault diagnosis because of its excellent advantages in data analysis. However, in practical industrial scenarios, the capability of intelligent fault diagnosis (IFD) method is still affected by two problems: (1) The signal samples provided for network learning are limited; (2) Fully extracting feature information from the original data is difficult. To address the above issues, a novel fault diagnosis method using joint learning network (JLNet) based on local-global feature perception is proposed. The method enhances the learning mechanism of fault signal through the local information dynamic perception subnetwork, which dynamically distinguishes between local impulse segment and normal signal segment. Then, a global channel attention mechanism (CAM) is used to guide the assignment of weights, which helps bidirectional gated recurrent unit (BiGRU) to learn advanced discriminative features. The feature information of the original signal is thoroughly mined through local-global comprehensive perception, thus realizing efficient diagnosis. In addition, the variation of the characteristics of each layer is analyzed by visualization, which improves the interpretability of the network. Finally, experiments are conducted using two different datasets, and the results show that JLNet has a better diagnostic effects and robustness.
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Characteristics of vibration response of ball bearing with local defect considering skidding
Yu Tian,Changfeng Yan,Yaofeng Liu,Wei Luo,Jianxiong Kang,Zonggang Wang,Lixiao Wu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.11
The occurrence and aggravation of local defects in ball bearings are closely linked to the skidding behavior of the ball. Previous studies have given less attention to investigating the impact of localized defects on the problem of bearing skidding. To investigate the dynamic response of defective bearings due to skidding, a dynamic model of the ball bearing is developed that considers various factors, including self-rotation, revolution, and radial motion of the ball, as well as the contact forces and friction forces of ball/cage and ball/race, time-varying displacement excitation, and elastohydrodynamic lubrication (EHL). Experimental signals collected from a machinery fault simulator test rig are used to validate the accuracy of the proposed model. The impact of race defects on the vibration characteristics of the bearing is analyzed, and the patterns of variation in contact and friction forces within one cycle of inner race rotation are described. The results indicate that the presence of defects intensifies the force fluctuation of the ball and causes it to deviate from its normal rolling condition. By comparing the skidding characteristics of a healthy bearing with a defective one under slippage, local defects will increase the skidding ratio of bearings. The proposed model can investigate the impact of race defects on the vibration response of ball bearings under the skidding condition.
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PC-SAN: Pretraining-Based Contextual Self-Attention Model for Topic Essay Generation
( Fuqiang Lin ),( Xingkong Ma ),( Yaofeng Chen ),( Jiajun Zhou ),( Bo Liu ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.8
Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.
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