Cited by 208Machine Fault Diagnosis TechniquesAdvanced machining processes and optimizationEngineering Technology and Methodologies

Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification

Cao, Xin-Cheng, Chen, Bin-Qiang, Yao, Bin, He, Wang-Peng·2019·Computers in Industry

Integrity signals

Registry

Crossref · 10.1016/j.compind.2018.12.018

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57 of 57 references verified0 citation errors · 0 not checkable

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  • Signal processing methods are employed to preprocess 1D data to convert to time-frequency domain so CNN can handle 2D time-frequency spectrum.

    “First, the signal processing methods, such as the wavelet packet [369][370][371], the continue wavelet transform [372,373], the dual-tree complex wavelet transform [374,375], and the synchrosqueezing transform [376], are employed to preprocess the input 1D data, which are expected to convert the signals from the time domain to the time-frequency domain.”

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Authors
Cao, X.-C., Chen, B.-Q., Yao, B., & He, W.-P.
Affiliations
Xiamen University (CN)
Venue
Computers in Industry
Publisher
Elsevier BV
Published
2019-04
Volume
106
Pages
71-84
ISSN
0166-3615 (ISSN-L)
OpenAlex
W2909430258
Cited by
208
References
57
Citations by year
2026: 15 · 2025: 18 · 2024: 25 · 2023: 38 · 2022: 38 · 2021: 37 · 2020: 28 · 2019: 9
FWCI
15.51
Type
journal article
Language
en
Open access
closed
Locations
Computers in Industry (publishedVersion)
Funders
  • National Natural Science Foundation of China (51605403)
  • Natural Science Foundation of Guangdong Province, China (2015A030310010)
  • Natural Science Foundation of Fujian Province, China (2016J01012)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Mechanical Engineering › Advanced machining processes and optimization
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Engineering Technology and Methodologies
Keywords
2D convolutional neural network, end milling, S45C steel, machining parameters, machine condition monitoring, feature extraction

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