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Your search keyword '"Liu, Hengchang"' showing total 14 results
Start Over Author "Liu, Hengchang" Publisher elsevier bv
14 results on '"Liu, Hengchang"'

7. Establishment and Validation of a Novel Promising Recurrence Prediction Molecular Subtype Classification for Patients with Stage II Colorectal Cancer

Author

Jiang, Zheng, primary, Wei, Ran, additional, Bu, Dechao, additional, Li, Shuofeng, additional, Yu, Guanhua, additional, Guan, Xu, additional, Chen, Haipeng, additional, Liu, Hengchang, additional, Quan, Jichuan, additional, Zhao, Zhixun, additional, Zhuang, Meng, additional, Hu, Xiyue, additional, Zhang, Mingguang, additional, Liu, Zheng, additional, He, Zihao, additional, Jin, Hui, additional, Li, Chunxiang, additional, Zhao, Yi, additional, and Wang, Xishan, additional

Published
2021
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9. Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit

Author

Dechen Yao, Limin Jia, Liu Hengchang, Jianwei Yang, and Boyang Li

Subjects
Artificial neural network, SIMPLE (military communications protocol), Computer science, Applied Mathematics, 020208 electrical & electronic engineering, 010401 analytical chemistry, Feature extraction, Pooling, SIGNAL (programming language), 02 engineering and technology, Condensed Matter Physics, computer.software_genre, 01 natural sciences, Convolutional neural network, 0104 chemical sciences, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Electrical and Electronic Engineering, Layer (object-oriented design), Focus (optics), Instrumentation, computer
Abstract

To overcome the shortcomings of traditional roller bearing remaining useful life prediction methods, which mainly focus on prediction accuracy improvement and ignore labor cost and time, the present work proposed a novel prediction method by combining an improved one-dimensional convolution neural network (1D-CNN) and a simple recurrent unit (SRU) network. For feature extraction, the proposed method uses the ability of the 1D-CNN to extract signal features. Moreover, use the global maximum pooling layer to replace the fully connected layer. In the prediction part, a parallel-input SRU network was established by reconstructing the serial operation mode of a traditional recurring neural network (RNN). Finally, experiments were carried out using the XJTU-SY dataset to verify. Results revealed that on the premise of ensuring prediction accuracy, the 1D-CNN-SRU method could reduce manual intervention and time cost to a certain extent and provide an intelligent method for roller bearing remaining useful life prediction.

Published
2021

10. A lightweight neural network with strong robustness for bearing fault diagnosis

Author

Jianwei Yang, Liu Hengchang, Xi Li, and Dechen Yao

Subjects
Artificial neural network, business.industry, Computer science, Applied Mathematics, Feature extraction, Pattern recognition, Fault severity, Condensed Matter Physics, Residual, Convolutional neural network, Robustness (computer science), Diagnostic equipment, Artificial intelligence, Electrical and Electronic Engineering, business, Instrumentation
Abstract

Traditional methods of rolling bearing fault diagnosis generally have the following disadvantages: low accuracy of fault severity identification, the need for artificial feature extraction, poor noise resistance and high requirements for diagnostic equipment. To overcome these disadvantages, an intelligent bearing fault diagnosis method based on Stacked Inverted Residual Convolution Neural Network (SIRCNN) is proposed. Compared with machine learning and classical convolutional neural networks, SIRCNN has a smaller model size, faster diagnosis speed and extraordinary robustness. The lightweight of the model is achieved through the application of depthwise separable convolution. Moreover, using the inverted residual structure ensures the accuracy of the model in noisy environments. The experimental results show that the fault diagnosis of rolling bearing based on SIRCNN can effectively identify the type and severity of bearing fault under different noise environments, improve the diagnostic efficiency and reduce the performance requirements for the diagnostic equipment.

Published
2020

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