Research on the Reconstruction Method of Missing Data of Mechanical Failure Based on Bayesian Meta-Learning

In unmanned systems, motor failures are frequent, and multi-sensor data fusion technology is an important strategy to improve the diagnostic performance. Aiming at the common missing problem in data fusion, this paper proposes a data reconstruction method based on Bayesian meta-learning (RIBM). First, the parallel self-learning network is used to extract fault features, and a priori weighting mechanism is constructed to maintain the time-frequency characteristics and mean values of the data, and to predict the variance; secondly, based on the a priori weighting mechanism, the data reconstruction network generates the complete reconstructed data and sets constraints to ensure that the samples are as close as possible to the real data; finally, for the network bias and feature degradation caused by the high missing rate, the feature regularization based on the Bayesian neural network is employed regularization method, which uses data uncertainty to reduce the bias. The experimental results verify that the method can effectively reconstruct the data, improve the diagnostic accuracy, and outperform the existing techniques.