A novel method for machine tool structure condition monitoring based on knowledge graph

被引:10
作者
Qiu, Chaochao [1 ,2 ]
Li, Bin [1 ,2 ]
Liu, Hongqi [2 ]
He, Songping [1 ]
Hao, Caihua [3 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Natl NC Syst Engn Res Ctr, Sch Mech Sci & Engn, 1037 Luoyu Rd, Wuhan 430074, Peoples R China
[3] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430074, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Machine tool; Structure health monitoring; Knowledge graph; FAULT-DIAGNOSIS; MODAL-ANALYSIS; ONTOLOGY; BRIDGE; IDENTIFICATION; DESIGN; REPRESENTATION; SYSTEM;
D O I
10.1007/s00170-022-08757-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
During a long-term structural health monitoring (SHM), large-scale machine tool structural response observed from various sensors show obvious big data characteristics. However, serious "data island" issues existing in traditional SHM inevitably limit the efficiencies of sensor data analysis. Besides, most existing identification methods for these diverse and complex signal data are manual methods, which are extremely time-consuming and inefficient. In this paper, a novel method based on knowledge graph (KG) was proposed to deal with the fine-grained domain knowledge modeling and multi-source sensor data integration problems in the field of machine tool SHM. With the help of KG-based querying and reasoning, it is more intelligent and convenient to retrieve and analyze sensor data. To verify the effectiveness of the proposed method, a long-term condition monitoring experiment was conducted on a CNC machine tool in an automobile factory. The dynamic properties of the machine tool structure were automatically identified based on KG, and a condition monitoring indicator based on the similarity of dynamic properties was applied to monitor the health condition of the machine tool. The final result showed the effectiveness of the KG for health monitoring of the machine tool structure.
引用
收藏
页码:563 / 582
页数:20
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