A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox

By Luyang Jing, Ming Zhao, Pin Li, Xiaoqiang Xu. 2017

640 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/9f9e47392b3194586accd652db0156323691bdf9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Jing Lin
2. Kaixuan Liang
3. Jinyang Jiao
4. Ming Zhao
5. Dongxiang Jiang
6. Chao Liu
7. Qin Wang
8. Olga Fink

In Mechanical Engineering

1. Jing Lin
2. Kaixuan Liang
3. Jinyang Jiao
4. Ming Zhao
5. Dongxiang Jiang
6. Chao Liu
7. Qin Wang
8. Olga Fink

Citations in the corpus

• A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion (2020, background): “Currently, vibration signals (VSs) are the most popular method for machine health monitoring because of their ability to convey intrinsic information on the health conditions of mechanical systems [2].”
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, background): “The same idea has been widely adopted for motor fault diagnosis (Ince et al., 2016), and broader PHM applications (Babu et al., 2016; Jing et al., 2017; Li et al., 2018; Wang et al., 2019b).”
• Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis (2020, neutral): “D) CNN (1DCNN) [12]–[14], [16], [17], [19], [23], [24]; the other converts the 1-”
• Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, methodology): “The works in [19] utilized 1D CNN to adaptively learn sensitive features from raw frequency spectrum of the data and achieved high diagnosis rates for combined gear-bearing-shaft faults in gearbox.”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “ethod. In [130], Dong et al. used 1- D CNN and 2-D CNN to respectively extract features from the frequency spectrum and STFT spectrum of vibration signals for rolling bearing degradation. Jing et al. [131] developed a CNN based method to 25 extract features from the frequency spectrum of vibration s”