方案(数学)
计算机科学
有限元法
生成语法
对抗制
样品(材料)
人工智能
故障检测与隔离
断层(地质)
模式识别(心理学)
方位(导航)
数据挖掘
工程类
结构工程
数学
执行机构
数学分析
地质学
地震学
化学
色谱法
作者
Yun Gao,Xiaoyang Liu,Jiawei Xiang
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2020-07-01
卷期号:16 (7): 4961-4971
被引量:103
标识
DOI:10.1109/tii.2020.2968370
摘要
Complete fault sample is essential to activate artificial intelligent (AI) models. A novel fault detection scheme is proposed to build a bridge between AI and real-world running mechanical systems. First, the finite element method simulation is used to simulate samples with different faults to overcome the shortcoming of missing fault samples. Second, to enlarge datasets, new samples similar to the simulation and measurement fault samples are generated by generative adversarial networks and further combined with the original simulation and measurement samples to obtain synthetic samples. Finally, the synthetic and unknown fault samples are severed as the training and test samples, respectively, to the classifiers of AI models, and the unknown fault types will be finally determined. A public datasets of bearings have been used to verify the effectiveness of the proposed scheme. It is expected that the proposed scheme can be extended to complex mechanical systems.
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