TY - JOUR
T1 - Fault diagnosis method based on time domain weighted data aggregation and information fusion
AU - Zhang, Yu
AU - Jiang, Wen
AU - Deng, Xinyang
N1 - Publisher Copyright:
© The Author(s) 2019.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - data aggregation
KW - Dempster combination rule
KW - Fault diagnosis
KW - information fusion
KW - linear decaying weights
UR - http://www.scopus.com/inward/record.url?scp=85073048064&partnerID=8YFLogxK
U2 - 10.1177/1550147719875629
DO - 10.1177/1550147719875629
M3 - 文章
AN - SCOPUS:85073048064
SN - 1550-1329
VL - 15
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
IS - 9
ER -