Wavelets for fault diagnosis of rotary machines: A review with applications

By Ruqiang Yan, Robert X. Gao, Xuefeng Chen. Signal Processing, 2014

1,410 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/893449c4a8df22c8d9c2891639f15ad8b2885c0e

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In Control and Systems Engineering

In Mechanics of Materials

Citations in the corpus

A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier (2018, methodology): “Time-domain in [2, 3], frequency-domain in [4 – 6], and time – frequency analysis techniques in [7 – 10] have been applied as signal-based systems for extracting the health information from the measured data.”
Variational mode decomposition denoising combined the detrended fluctuation analysis (2016, methodology): “The fundamental principle of wavelet filtering [12,13] is that a thresholding rule is applied to wavelet coefficients, and then the inverse discrete wavelet transform (IDWT) reconstructs the signal from these filtered wavelet coefficients.”
Machine learning methods for wind turbine condition monitoring: A review (2019, neutral): “[65].”
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, methodology): “For the WPD, we use Daubechies 4 wavelet to decompose each sample into four levels, thus producing 16 nodes in different frequency bands.”
Bayesian Networks in Fault Diagnosis (2017, background): “In general, fault diagnosis approaches can be classified into three categories: model-based [1], [2], signal-based [3]–[5], and”
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains (2020, methodology): “It can convert the time-domain signal into a series of wavelet coefficients in timescale domain [28].”
Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning (2017, background): “…which fall into three main categories: time-domain analysis (Zhou et al, 2008; Parey and Pachori, 2012), frequency domain-analysis (Fakhfakh et al, 2005; Li et al, 2015; Wen et al, 2015) and time-frequency analysis (Tang et al, 2010; Chaari et al, 2012; Yan et al, 2014, Zhang and Tang, 2018).”