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基于层次密度峰值聚类和最相似模态的故障监测方法
Alternative TitleFault monitoring method based on hierarchical density peak clustering and most similar mode
李帅; 周晓锋; 史海波; 潘福成; 李歆; 张宜驰
Department数字工厂研究室
Rights Holder中国科学院沈阳自动化研究所
Patent Agent21002 沈阳科苑专利商标代理有限公司
Country中国
Subtype发明授权
Status有权
Abstract本发明涉及一种基于层次密度峰值聚类和最相似模态的故障监测方法,对工业过程历史正常数据进行模态划分,获取层次模态信息;利用层次模态信息对工业过程历史正常数据建立故障监测模型;将待监测的工业过程数据,得到最相似模态,输入到所述故障监测模型,进行故障监测。本发明利用已有的工业数据资源,考虑了复杂工业过程的多模态性和多模态动态性和不确定性,克服现有多模态故障监测方法依赖先验的模态信息,采用固定的模态划分及模型等局限,这对于及时发现工业过程的异常工况、保证生产安全、提高产品质量具有重要意义。
Other AbstractThe invention relates to a fault monitoring method based on hierarchical density peak clustering and the most similar mode. The historical normal data of an industrial process are modally divided to acquire hierarchical modal information. The hierarchical modal information is used to establish a fault monitoring model for the historical normal data of the industrial process. The most similar modeof the industrial process data to be monitored is acquired and input into the fault monitoring model for fault monitoring. According to the invention, existing industrial data resources are used; multimodality and multimodal dynamics and uncertainty of a complex industrial process are considered; the limitations of relying on priori modal information and using fixed modal dividing and models of the existing multimodal fault monitoring method are overcome; and the method is important for timely detecting abnormal conditions in the industrial process, ensuring production safety and improving product quality.
PCT Attributes
Application Date2017-12-18
2019-06-25
Date Available2020-08-07
Application NumberCN201711365157.4
Open (Notice) NumberCN109933040B
Language中文
Contribution Rank1
Document Type专利
Identifierhttp://ir.sia.cn/handle/173321/27615
Collection数字工厂研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
李帅,周晓锋,史海波,等. 基于层次密度峰值聚类和最相似模态的故障监测方法[P]. 2019-06-25.
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