Journal ArticleDOI
Fault diagnosis for rolling bearing based on VMD-FRFT
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TLDR
The VMD-FRFT proposed in this paper has certain reference significance for the fault diagnosis of rolling bearings and can provide an effective filtering algorithm for the extraction of fundamental frequency and frequency multiplication of instantaneous frequency.About:
This article is published in Measurement.The article was published on 2020-04-01. It has received 66 citations till now. The article focuses on the topics: Fractional Fourier transform & Fourier transform.read more
Citations
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Journal ArticleDOI
Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery
TL;DR: Several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis are discussed.
Journal ArticleDOI
An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions
TL;DR: A domain adaptation framework based on multiscale mixed domain feature (DA-MMDF) for cross-domain intelligent fault diagnosis of rolling bearings under polytropic working conditions is established and the analysis result confirms that DA- MMDF is able to achieve effective transfer diagnosis between polytropical working conditions.
Journal ArticleDOI
Accuracy-improved bearing fault diagnosis method based on AVMD theory and AWPSO-ELM model
TL;DR: An accuracy-improved bearing fault diagnosis method based on adaptive parameter optimized variational mode decomposition (AVMD) theory and extreme learning machine optimized by adaptive weight particle swarm optimization (AWPSO-ELM) model is put forward and reaches an accuracy of 100% on the testing dataset.
Journal ArticleDOI
Adaptive energy-constrained variational mode decomposition based on spectrum segmentation and its application in fault detection of rolling bearing
TL;DR: In this paper, an adaptive energy-constrained VMD method based on spectrum segmentation was proposed for rolling bearing fault detection. But the performance of the VMD algorithm is highly dependent on the input parameters: the number of modes, the penalty parameter and even the initial center frequency (ICF).
Journal ArticleDOI
Intelligent fault diagnosis of diesel engine via adaptive VMD-Rihaczek distribution and graph regularized bi-directional NMF
TL;DR: A novel intelligent fault diagnosis framework using the variational mode decomposition (VMD) and Rihaczek distribution and a novel graph regularized bi-directional non-negative matrix factorization (GBiNMF) algorithm to find a parts-based representation of the TFRs corresponding to different fault models.
References
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Journal ArticleDOI
Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu,Norden E. Huang +1 more
TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
Journal ArticleDOI
Variational Mode Decomposition
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Journal ArticleDOI
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Akhand Rai,S. H. Upadhyay +1 more
TL;DR: In this article, the authors have presented the various signal processing methods applied to the fault diagnosis of rolling element bearings with the objective of giving an opportunity to the examiners to decide and select the best possible signal analysis method as well as the excellent defect representative features for future application in the prognostic approaches.
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A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery
TL;DR: In this paper, the authors proposed a parameter-adaptive variational mode decomposition (VMD) method based on grasshopper optimization algorithm (GOA) to analyze vibration signals from rotating machinery.
Journal ArticleDOI
Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive
TL;DR: In this paper, an independence-oriented VMD method via correlation analysis is proposed to adaptively extract weak and compound fault feature of wheel set bearing of high speed locomotive, and then the similar modes are combined according to the similarity of their envelopes to solve the over decomposition problem.