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

Zhenpeng Teng, Yongai Hou, Biao Wang*, Xiaojian Yi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 4th 2024 International Conference on Autonomous Unmanned Systems, 4th ICAUS 2024 - Volume VII
EditorsLianqing Liu, Yifeng Niu, Wenxing Fu, Yi Qu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages347-356
Number of pages10
ISBN (Print)9789819635917
DOIs
Publication statusPublished - 2025
Event4th International Conference on Autonomous Unmanned Systems, ICAUS 2024 - Shenyang, China
Duration: 19 Sept 202421 Sept 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1380 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference4th International Conference on Autonomous Unmanned Systems, ICAUS 2024
Country/TerritoryChina
CityShenyang
Period19/09/2421/09/24

Keywords

  • Bayesian meta-learning
  • Data reconstruction
  • Missing data

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