A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data-Reference-Cited by-同舟云学术

A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data

Published:2023 Issue:3 Volume:20 Page:5223-5242
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Yang Jing¹,Xie Guo²,Yang Yanxi²,Li Qijun¹,Yang Cheng¹

Affiliation:

1. School of Mechatronics and Automotive Engineering, Tianshui Normal University, Tianshui 741000, China

2. School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China

Abstract

<abstract> <p>As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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