Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks-Reference-Cited by-同舟云学术

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Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks

Published:2020-01-15 Issue:4 Volume:31 Page:045016
ISSN:0957-0233
Container-title:Measurement Science and Technology
language:
Short-container-title:Meas. Sci. Technol.

Author:

Shu Qing,Lu Siliang^ORCID,Xia Min,Ding Jianming,Niu Jiahao,Liu Yongbin^ORCID

Funder

National Natural Science Foundation of China

The Open Project Program of the Traction Power State Key Laboratory of the Southwest Jiaotong University

The Open Project Program of the State Key Laboratory of Mechanical Transmissions of the Chongqing University

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Link

https://iopscience.iop.org/article/10.1088/1361-6501/ab5cca/pdf

Reference34 articles.

1. Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review

2. A Simple, Fast, and Robust Open-Phase Fault Detection Technique for Six-Phase Induction Motor Drives

3. Multi-Scale Stochastic Resonance Spectrogram for fault diagnosis of rolling element bearings

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1. A fault diagnosis method for motor vibration signals incorporating Swin transformer with locally sensitive hash attention;Measurement Science and Technology;2024-01-25

2. A novel deep convolutional neural network and its application to fault diagnosis of the squirrel-cage asynchronous motor under noisy environment;Measurement Science and Technology;2023-08-02

3. Image deep learning in fault diagnosis of mechanical equipment;Journal of Intelligent Manufacturing;2023-07-15

4. Load and Vibration Optical Fiber Sensor Based on Miniature Fabry–Perot Cavity Encapsulated by Micrometer Thickness of Silica Film;IEEE Sensors Journal;2023-06-15

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