Fast Fault Diagnosis System Based on Data Mining AR Algorithm | |
Yu Yahan1,3; Du Juan2; Ren Guanghao3; Tan Yao2; Wang Jian3; Zhang Guigang3 | |
2021-10 | |
会议日期 | 2021-10 |
会议地点 | Nanjing, China |
英文摘要 | Aero-mechanical parts are an important part of the aircraft, and the maintenance of their failures also consumes a lot of manpower and financial resources. Therefore, the fault diagnosis research of aero-mechanical parts is of great significance for ensuring the safety of human life and reducing economic losses. With the development of fault diagnosis technology, the monitoring data is becoming more and more abundant and complex. The traditional methods of processing and analyzing the monitoring data have become more difficult, and it is difficult to establish accurate mathematical models. Therefore, the rapid diagnosis method of aviation machinery parts Become the research focus of fault diagnosis. This paper constructs a rapid fault diagnosis system for the construction of aviation machinery parts. Based on the input of past cases, new cases, literature cases, and book knowledge, the case library is refined and the graph library and rule term library are added. AR algorithm is used to mine and obtain Useful association rules between the decision attributes (failure mode, failure mechanism, failure reason, etc.) of the failure information in the database and the basic attributes (basic information other than the decision attributes), to achieve the purpose of assisting failure analysts in rapid fault diagnosis. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/51871] |
专题 | 数字内容技术与服务研究中心_智能技术与系统工程 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 2.Chengdu Aircraft Industrial (Group) Co., LTD., Aviation Industry Corporation of China, LTD., Chengdu, China 3.Institute of Automation Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Yu Yahan,Du Juan,Ren Guanghao,et al. Fast Fault Diagnosis System Based on Data Mining AR Algorithm[C]. 见:. Nanjing, China. 2021-10. |
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