Missing signal imputation for multi-channel sensing signals on rotary machinery by tensor factorization

Abstract

Multi-channel sensor fusion can be challenging for real-time machinery fault identification and diagnosis when a substantial amount of missing data exists. Usually, some (or even all) sensors may not function correctly during real-time data acquisition due to sensor malfunction or transmission issues. Additionally, multi-channel sensor fusion yields a large volume of data. Imputation of missing entries can also be challenging with a large volume of data, which can predominantly affect the accuracy of machinery fault diagnosis. However, how to impute a substantial amount of missing data for machinery fault identification is an open research question. In light of the above challenges, this paper proposes constructing time-domain tensors based on heterogeneous sensor signals. Subsequently, the fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method is adopted for missing data imputation of diverse bearing faults signals. To validate the effectiveness of this proposed method, a machinery fault simulator was used to collect diverse bearing fault signals by incorporating both acoustics and vibration sensors. A varying percentage of continuous missing signal scenarios are introduced at the random locations among different acoustics and vibration channels to construct incomplete tensors. Subsequently, the FBCP method was leveraged to complete the incomplete tensors and calculate estimated tensors. To evaluate the performance of continuous missing data imputation, relative standard errors are computed based on the estimated and actual time-domain tensors. Experimental results show that this proposed method can effectively impute a substantial portion of continuous missing data from diverse bearing fault scenarios.