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MBNN: A Multi-Branch Neural Network Capable of Utilizing Industrial Sample Unbalance for Fast Inference
Wang QZ(王其朝)1,2,3,4; Wang K(王锴)1,2,3; Li Q(李庆)1,2,3,4; Yang, Zuye5; Jin, Guangshu5; Wang H(王宏)1,2,3
Department工业控制网络与系统研究室
Source PublicationIEEE SENSORS JOURNAL
ISSN1530-437X
2021
Volume21Issue:2Pages:1809-1819
Indexed BySCI
WOS IDWOS:000600900300101
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China [2017YFE0123000]
KeywordFault diagnosis Neural networks Complexity theory Computational modeling Sensors Optimization Real-time systems Neural network architecture fault diagnosis fast inference unbalance sample
Abstract

Deep neural networks has been widely used in industrial equipment fault diagnosis. The accuracy of deep neural network is usually proportional to the complexity, but the high inference delay and energy consumption caused by the complex model make it difficult to be applied in the industrial environment of real-time demand. At the same time, in the diagnosis of industrial equipment, different categories of samples have unbalanced characteristics in terms of number, difficulty of identification, and demand of identification. In order to solve this problem, this paper designs Multi-Branch Neural Network (MBNN), which is a new type neural network architecture that can use the unbalance of sample categories in industrial equipment fault diagnosis for fast inference. MBNN has multiple sub-networks with different complexity, and each branch is responsible for processing different categories of samples. Categories with large numbers, easy to process, and high demand of identification are processed through simple branches, such as normal samples. Categories with small numbers, difficult to identification, and low demand of identification are processed through complex branches, such as potential failure samples. The feasibility of MBNN has been verified on motor bearing fault diagnosis and gearbox fault diagnosis, and its performance has been evaluated on multiple computing platforms. The results show that MBNN can greatly improve the inference speed while ensuring the recognition accuracy, especially on resource-constrained platforms.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28141
Collection工业控制网络与系统研究室
Corresponding AuthorWang H(王宏)
Affiliation1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110169, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.Microcyber Corporation, Shenyang 110179, China
Recommended Citation
GB/T 7714
Wang QZ,Wang K,Li Q,et al. MBNN: A Multi-Branch Neural Network Capable of Utilizing Industrial Sample Unbalance for Fast Inference[J]. IEEE SENSORS JOURNAL,2021,21(2):1809-1819.
APA Wang QZ,Wang K,Li Q,Yang, Zuye,Jin, Guangshu,&Wang H.(2021).MBNN: A Multi-Branch Neural Network Capable of Utilizing Industrial Sample Unbalance for Fast Inference.IEEE SENSORS JOURNAL,21(2),1809-1819.
MLA Wang QZ,et al."MBNN: A Multi-Branch Neural Network Capable of Utilizing Industrial Sample Unbalance for Fast Inference".IEEE SENSORS JOURNAL 21.2(2021):1809-1819.
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