TY - GEN
T1 - Fault Trend Prediction of Centrifugal Blowers Considering Incomplete Data
AU - Zhang, You
AU - Li, Congbo
AU - Tang, Ying
AU - Zhou, Feng
AU - Zhang, Xu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Centrifugal blowers have high failure frequency due to the harsh working environment, and it is urgent to perform fault trend prediction for predictive maintenance. Centrifugal blowers often have missing data due to sensor failures and unstable network connections, which will lose a lot of useful information. To enhance the prediction accuracy and stability, a fault trend prediction method considering incomplete data is proposed. The tensor decomposition is used to fill the missing data to obtain the complete data of the centrifugal blower. Based on the filled complete data, the deep belief network (DBN) is used to construct a health indicator that can characterize the health status of the centrifugal blower. The fault prediction model built by Informer is developed to predict the degradation trend of health indicators to realize the fault trend prediction of the centrifugal blower. Experimental results demonstrate that the prediction model established by the filled data can predict the fault earlier than missing data. The proposed Informer method has superior prediction performance than other methods.
AB - Centrifugal blowers have high failure frequency due to the harsh working environment, and it is urgent to perform fault trend prediction for predictive maintenance. Centrifugal blowers often have missing data due to sensor failures and unstable network connections, which will lose a lot of useful information. To enhance the prediction accuracy and stability, a fault trend prediction method considering incomplete data is proposed. The tensor decomposition is used to fill the missing data to obtain the complete data of the centrifugal blower. Based on the filled complete data, the deep belief network (DBN) is used to construct a health indicator that can characterize the health status of the centrifugal blower. The fault prediction model built by Informer is developed to predict the degradation trend of health indicators to realize the fault trend prediction of the centrifugal blower. Experimental results demonstrate that the prediction model established by the filled data can predict the fault earlier than missing data. The proposed Informer method has superior prediction performance than other methods.
UR - http://www.scopus.com/inward/record.url?scp=85174425558&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174425558&partnerID=8YFLogxK
U2 - 10.1109/CASE56687.2023.10260427
DO - 10.1109/CASE56687.2023.10260427
M3 - Conference contribution
AN - SCOPUS:85174425558
T3 - IEEE International Conference on Automation Science and Engineering
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -