期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：9
页码：1
DOI：10.1177/1550147719875629
出版社：Hindawi Publishing Corporation
摘要：Fault diagnosis of equipment is a key issue in the industrial field, and it is essential to keep abreast of equipment status. However, previous studies either considered fault data at a single moment or gave the same weight to data over a period of time. In view of the problems above, fault diagnosis method based on time domain weighted data aggregation and information fusion is proposed in this article. First, the monitored data of sensors loaded by the equipment are aggregated utilizing the linear decaying weights. Then, Gaussian models of each fault type under different fault features are established based on aggregated data. And the basic probability assignments are generated by matching aggregated testing samples with the constructed Gaussian model. At last, the basic probability assignments generated under each fault feature are fused by Dempster combination rule. The proposed method is verified and the results show that the total fault recognition rate can reach 97.5%, which increased by 1.9% compared with the method that Gaussian model constructed by original data.
关键词：Fault diagnosis; linear decaying weights; data aggregation; information fusion; Dempster combination rule
其他关键词：Fault diagnosis ; linear decaying weights ; data aggregation ; information fusion ; Dempster combination rule
