Rolling bearing fault diagnosis based on quantum LS-SVM

被引:12
作者
Li, Yuanyuan [1 ]
Song, Liyuan [1 ]
Sun, Qichun [1 ]
Xu, Hua [2 ]
Li, Xiaogang [3 ]
Fang, Zhijun [1 ]
Yao, Wei [1 ]
机构
[1] Shanghai Univ Engn Sci, Shanghai, Peoples R China
[2] Kunfeng Quantum Technol Co Ltd, Shanghai, Peoples R China
[3] Yiwei Quantum Technol Co Ltd, Shenzhen, Peoples R China
基金
国家重点研发计划;
关键词
Rolling bearing; Fault diagnosis; Quantum computing; LS-SVM; HHL;
D O I
10.1140/epjqt/s40507-022-00137-y
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Rolling bearing is an indispensable part of the contemporary industrial system, and its working conditions affect the state of the entire industrial system. Therefore, there is great engineering value to researching and improving the fault diagnosis technology of rolling bearings. However, with the involvement of the whole mechanical equipment, we need to have a large quantity of data to support the accuracy of fault diagnosis, while the efficiency of classical machine learning algorithms is poor in processing big data, and huge amount of computing resources is required. To solve this problem, this paper combines the HHL algorithm in quantum computing with the LS-SVM algorithm in machine learning and proposes a fault diagnosis model based on a quantum least square support vector machine (QSVM). Based on experiments simulated on analog quantum computers, we demonstrate that our fault diagnosis based on QSVM is feasible, and it can play a far superior advantage over the classical algorithm in the context of big data.
引用
收藏
页数:15
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