2020
DOI: 10.1109/access.2020.2982213
|View full text |Cite
|
Sign up to set email alerts
|

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

Abstract: As a part of the energy transmission chain, gearboxes are considered as important components in rotating machines, and the gearbox failure results in costly economic losses. Therefore, it is necessary to detect the appearance of incipient gearbox faults by implementing an appropriate detected model. The incipient failure characteristics of the gearbox are weak and hidden in a set of time-varying series signals the vibration signals, which is difficult to effectively extract under the background of strong noise… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
8
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 23 publications
(8 citation statements)
references
References 40 publications
0
8
0
Order By: Relevance
“…Yu et al 196 employed dynamic ICA (DICA) combined with auto-associative kernel regression for fault detection to address the nonlinearity and multimodality in complex industrial systems. Shi et al 198 introduced an incipient fault detection method in gearbox systems using Deep Recursive Dynamic Principal Component Analysis, in order to detect faults in time-varying, noisy vibration signals. Ahmad et al 199 introduced an RUL estimation method for bearing based on the dynamic regression models.…”
Section: Dynamic Methodsmentioning
confidence: 99%
“…Yu et al 196 employed dynamic ICA (DICA) combined with auto-associative kernel regression for fault detection to address the nonlinearity and multimodality in complex industrial systems. Shi et al 198 introduced an incipient fault detection method in gearbox systems using Deep Recursive Dynamic Principal Component Analysis, in order to detect faults in time-varying, noisy vibration signals. Ahmad et al 199 introduced an RUL estimation method for bearing based on the dynamic regression models.…”
Section: Dynamic Methodsmentioning
confidence: 99%
“…Experiments show that under the condition of Gaussian and non-Gaussian interference and the resonance frequencies of multiple faults are easily coupled with each other, the LEASgram method has significant fault identification and separation capabilities. In recent years, there are many new feature strategies partially based AI technologies designed for gearbox such as multiclass relevance vector machine (mRVM) [130], multiview sparse (MVSF) [8], deep recursive dynamic principal component analysis (Deep RDPCA) [131], Resonance Residual Technique [132] and deep learning based methods [133], [134].…”
Section: B Ai Approaches For Detecting Faults Of Transmissionmentioning
confidence: 99%
“…erefore, x 11 (t) is a signal where quadratic phase coupling is absent, except over a relatively short-time interval (blocks [13][14][15][16].…”
Section: Comparison Of Fourier-based Bicoherence and Wavelet-mentioning
confidence: 99%
“…us, the diagnosis of gear faults has significant importance for both safety and reliability [4][5][6][7][8][9]. Vibration signal processing is widely used to detect gear faults, but its application is limited by transducer installation [10][11][12][13][14][15]. As the acceleration sensor does not have enough installation space or brings additional maintenance costs, the vehicle becomes more complicated [16].…”
Section: Introductionmentioning
confidence: 99%