Cited by 200Machine Fault Diagnosis TechniquesFault Detection and Control SystemsNeural Networks and Applications

Application of recurrent neural network to mechanical fault diagnosis: A review

Zhu, Junjun, Jiang, Quansheng, Shen, Yehu, Qian, Chenhui, Xu, Fengyu, Zhu, Qixin·2022·Journal of Mechanical Science and Technology

Integrity signals

Registry

Crossref · 10.1007/s12206-022-0102-1

Cites

110 of 110 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Zhu, J., Jiang, Q., Shen, Y., Qian, C., Xu, F., & Zhu, Q.
Affiliations
  • Suzhou University of Science and Technology (CN)
  • Nanjing University of Posts and Telecommunications (CN)
Venue
Journal of Mechanical Science and Technology
Published
2022-02
Volume
36
Issue
2
Pages
527-542
ISSN
  • 1738-494X (ISSN-L)
  • 1976-3824
OpenAlex
W4210741351
Cited by
200
References
110
Citations by year
2026: 38 · 2025: 56 · 2024: 59 · 2023: 37 · 2022: 10
FWCI
16.31
Type
journal article
Language
en
Open access
closed
Locations
Journal of Mechanical Science and Technology (publishedVersion)
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 › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
recurrent neural networks, mechanical fault diagnosis, time-series signals, deep feature learning, hybrid neural network, nonlinear system modeling, smart manufacturing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review