关键词: 离心鼓风机, 故障趋势预测, 不完备数据, Informer方法, 张量分解

Abstract: During the operation of the centrifugal blower,the missing monitoring data will cause the fault trend prediction lag and prediction accuracy to decrease.To solve this problem,a fault trend prediction method considering incomplete data was proposed.The missing monitoring data were filled by tensor decomposition to obtain the complete monitoring data.Based on the completed monitoring data,the Deep Belief Network (DBN) was used to construct the health indicators that could characterize the health status of the centrifugal blower.The Informer method was used to predict the future trend of health indicator and realized fault trend prediction of the centrifugal blower.Experiment results showed that the prediction model used the filled data could predict the occurrence of faults earlier than incomplete data.Meanwhile,the prediction accuracy of the proposed method was higher than Transformer,Long Short Term Memory (LSTM),Gate Recurrent Unit (GRU) and other conventional methods.

Key words: centrifugal blower, fault trend prediction, incomplete data, Informer method, tensor decomposition