Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification
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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.”
From Applications of machine learning to machine fault diagnosis: A review and roadmap (2020) · cites this paper as [375]
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Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997 · see citing works
- Authors
- Cao, X.-C., Chen, B.-Q., Yao, B., & He, W.-P.
- Affiliations
- Xiamen University (CN)
- ORCID
- Yao, Bin
- 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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