Literature DB >> 31395284

An improved local mean decomposition method based on improved composite interpolation envelope and its application in bearing fault feature extraction.

Xiang Li1, Jun Ma2, Xiaodong Wang1, Jiande Wu1, Zhuorui Li1.   

Abstract

In order to overcome the influence of non-adaptive selection of non-stationary coefficient threshold of compound interpolation envelope (CIE) method on decomposition performance of local mean decomposition (LMD), a LMD method based on improved compound interpolation envelope (ICIE) is proposed in this paper. Firstly, combining the CIE with fractal box dimension, an improved envelope processing method, named ICIE, is proposed. Secondly, an improved LMD-based ICIE is presented and abbreviated as ICIELMD. Finally, three different data-sets, including simulation signal, rolling bearing data-sets from Case Western Reserve University (CWRU) and National Aeronautics and Space Administration (NASA), are used to complete the comparative experiments between the proposed ICIELMD and state-of-the-art methods (CIELMD) and demonstrate the effectiveness of the ICIELMD method. The experimental results show that the proposed method achieves comparable or slightly better than the other methods, and provides a new solution for complex signal analysis of rolling bearing faults.
Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Fault feature extraction; Hilbert demodulation; Improved compound interpolation envelope; Improved local mean decomposition; Kurtosis criterion; Rolling bearings

Year:  2019        PMID: 31395284     DOI: 10.1016/j.isatra.2019.07.027

Source DB:  PubMed          Journal:  ISA Trans        ISSN: 0019-0578            Impact factor:   5.468


  4 in total

1.  Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index.

Authors:  Yuanjing Guo; Youdong Yang; Shaofei Jiang; Xiaohang Jin; Yanding Wei
Journal:  Sensors (Basel)       Date:  2022-05-20       Impact factor: 3.847

2.  A New Fault Diagnosis of Rolling Bearing Based on Markov Transition Field and CNN.

Authors:  Mengjiao Wang; Wenjie Wang; Xinan Zhang; Herbert Ho-Ching Iu
Journal:  Entropy (Basel)       Date:  2022-05-25       Impact factor: 2.738

3.  Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data.

Authors:  Ko-Chieh Chao; Chuan-Bi Chou; Ching-Hung Lee
Journal:  Sensors (Basel)       Date:  2022-06-16       Impact factor: 3.847

4.  Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions.

Authors:  Hongwei Ban; Dazhi Wang; Sihan Wang; Ziming Liu
Journal:  Sensors (Basel)       Date:  2021-05-06       Impact factor: 3.576

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.