Cited by 174Machine Fault Diagnosis TechniquesFault Detection and Control SystemsElectrical Fault Detection and Protection

Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis

Zhao, Minghang, Kang, Myeongsu, Tang, Baoping, Pecht, Michael·2019·IEEE Transactions on Industrial Electronics

Abstract

Wavelet transform, an effective tool to decompose signals into a series of frequency bands, has been widely used for vibration-based fault diagnosis in machinery. Likewise, the use of deep learning algorithms is becoming popular to automatically learn discriminative features from input data for the sake of improving diagnostic performance. However, the fact that no general consensus has been reached as to which wavelet basis functions are useful for diagnosis motivated this investigation of methods to fuse multiple wavelet transforms into deep learning algorithms. In this paper, two methods-i.e., multiple wavelet coefficients fusion in deep residual networks by concatenation and multiple wavelet coefficients fusion in deep residual networks by maximization-were developed to capture discriminative information from diverse sets of wavelet coefficients for fault diagnosis. The efficacy of the developed methods was verified by applying them to planetary gearbox fault diagnosis.

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Crossref · 10.1109/tie.2018.2866050

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Authors
Zhao, M., Kang, M., Tang, B., & Pecht, M.
Affiliations
  • Chongqing University (CN)
  • University of Maryland, College Park (US)
Venue
IEEE Transactions on Industrial Electronics
Published
2019-06
Volume
66
Issue
6
Pages
4696-4706
ISSN
  • 0278-0046 (ISSN-L)
  • 1557-9948
OpenAlex
W2888337213
Cited by
174
References
32
Citations by year
2026: 2 · 2025: 20 · 2024: 14 · 2023: 28 · 2022: 38 · 2021: 27 · 2020: 30 · 2019: 13 · 2018: 1 · 2014: 1
FWCI
10.99
Type
journal article
Language
en
Open access
closed
Locations
IEEE Transactions on Industrial Electronics (publishedVersion)
Funders
  • National Natural Science Foundation of China (51775065)
  • Technology Innovation Program (or Industrial Strategic Technology Development Program (10076392)
  • Ministry of Trade, Industry and Energy
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
Keywords
wavelet coefficient fusion, ResNet, vibration fault diagnosis, mechanical fault diagnosis, planetary gearbox fault diagnosis, wavelet basis functions, feature fusion, discriminative features

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