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Research on Fault Diagnosis of Nuclear Gate Valve Based on Analysis of Energy Variation Characteristics of Vibration Signal

https://doi.org/10.1109/icphm53196.2022.9815758
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11/11 checkable references clean · checked 2026-07-22

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

7 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 11 checked references that resolve
resolves10.1109/ISRIMT53730.2021.9596987
Diagnosis of Weak Fault of Rolling Bearing Based on EEMD and Envelope Spectrum Analysis
resolves10.1109/ACCESS.2020.2970813
A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD
resolves10.1109/TIM.2019.2946470
Fault Identification for Circuit Breakers Based on Vibration Measurements
resolves10.1109/ICPHM49022.2020.9187051
Intelligent fault diagnosis of bearings based on feature model and Alexnet neural network
resolves10.1109/ACCESS.2020.3006030
An Improved Empirical Mode Decomposition Based on Adaptive Weighted Rational Quartic Spline for Rolling Bearing Fault Diagnosis
resolves10.1109/ACCESS.2020.3012559
Intelligent Fault Diagnosis of Rolling Bearing Using FCM Clustering of EMD-PWVD Vibration Images
resolves10.1109/PHM-Besancon49106.2020.00050
Bearing Fault Severity Classification Based on EMD-KLD : A Comparative Study for Incipient Ball Faults
resolves10.1109/YAC51587.2020.9337677
Bearing Fault Diagnosis Research Based on Empirical Mode Decomposition and Deep Learning
resolves10.1109/ICCCBDA51879.2021.9442505
Gearbox Fault Diagnosis Classification with Empirical Mode Decomposition Based on Improved Long Short-Term Memory
resolves10.1109/CCDC49329.2020.9164259
Research on locomotive motor bearing diagnosis method based on wavelet threshold denoising and correlation analysis combined with EMD decomposition and LSTM neural network
resolves10.1109/JSEN.2020.3041737
DC Arc-Fault Detection Based on Empirical Mode Decomposition of Arc Signatures and Support Vector Machine
The 7 references without a DOI — listed, not checked
no DOI — not checkedBearing fault diagnosis method based on EMD-VMD adjacent reconstruction and secondary decomposition
no DOI — not checkedAdaptive Suppression of Mode Mixing in CEEMD Based on Genetic Algorithm for Motor Bearing Fault Diagnosis
no DOI — not checkedNovel Health Diagnostics Schemes Analyzing Corona Discharge of Operational Aerial Bundled Cables in Coastal Regions
no DOI — not checkedRolling Bearing Fault Diagnosis Method Based on EEMD Permutation Entropy and Fuzzy Clustering
no DOI — not checkedDual-Ensemble Multi-Feedback Neural Network for Gearbox Fault Diagnosis
no DOI — not checkedAn Adaptive Optimized TVF-EMD Based on a Sparsity-Impact Measure Index for Bearing Incipient Fault Diagnosis
no DOI — not checkedAn EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

checked 2026-07-22 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record

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