An improved deep network for intelligent diagnosis of machinery faults with similar features-Reference-Cited by-同舟云学术

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An improved deep network for intelligent diagnosis of machinery faults with similar features

Published:2019-10-23 Issue:12 Volume:14 Page:1851-1864
ISSN:1931-4973
Container-title:IEEJ Transactions on Electrical and Electronic Engineering
language:en
Short-container-title:IEEJ Trans Elec Electron Eng

Author:

Yang Jing¹²,Xie Guo¹,Yang Yanxi¹,Li Xin¹,Mu Lingxia¹,Takahashi Sei³,Mochizuki Hiroshi³

Affiliation:

1. School of Automation and Information EngineeringXi'an University of Technology Xi'an 710048 China

2. School of Mechatronics and Automotive EngineeringTianshui Normal University Tianshui 741000 China

3. Department of Computer EngineeringCollege of Science and Technolog, Nihon University Funabashi 274‐8501 Chiba Japan

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.23012

Reference22 articles.

1. Weak fault diagnosis of rotating machinery based on feature reduction with Supervised Orthogonal Local Fisher Discriminant Analysis

2. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

3. A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM

4. Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis

5. Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network

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1. A feature reconstruction and SAE model based diagnosis method for multiple mixed faults;Measurement Science and Technology;2024-05-24

2. An Open-switch Fault Diagnosis Method for Single-phase PWM Rectifier Based on CEEMD-DNN;International Journal of Control, Automation and Systems;2023-10

3. VWM-DCRNN: A Method of Combining Signal Processing With Deep Learning for Fault Diagnosis in Single-Phase PWM Rectifier;IEEE Transactions on Power Electronics;2023-07

4. A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data;Mathematical Biosciences and Engineering;2023

5. Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification;IEEE Access;2023

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