Open AccessJournal Article
SVD component-envelope detection method and its application in the incipient fault diagnosis of rolling bearing
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TL;DR: In this article, an approach for incipient fault diagnosis using the component-envelope detection method is presented, where SVD is first used to reconstruct the one-dimensional sampling time sequence into high dimensions space and an inverse transformation is performed to change this subspace into component signal.
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Abstract: In order to diagnose the incipient fault of a rolling bearing,an approach for incipient fault diagnosis using the component-envelope detection method is presented in this paper.SVD is first used to reconstruct the one-dimensional sampling time sequence into high dimensions space.Then subspace containing fault feature information can be found.An inverse transformation is performed to change this subspace into component signal.With envelope analysis,the envelope spectrum of the component signal is obtained.Through the envelope spectrum,the incipient fault feature of rolling bearing can be detected obviously.Both simulation and engineering experimental results validate the effectiveness of the proposed method.
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Citations
A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors
Guoguo Wu,Tanyi Yan,Guolai Yang,H. Chai,Chuanchuan Cao +4 more
TL;DR: In this article , the authors summarized the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings and divided the fault signal types into one-dimensional and two-dimensional images.
Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization
TL;DR: Wang et al. as mentioned in this paper proposed a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization, which is suitable for matrix processing but challenged by the higher-order data.
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A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis
TL;DR: Wang et al. as discussed by the authors proposed a data-driven diagnostic algorithm based on the characteristics of bearing vibrations called multi-size kernel based adaptive convolutional neural network (MSKACNN), which provides vibration feature learning and signal classification capabilities to identify and analyze bearing faults.
Research on Fault Diagnosis Method of Rolling Bearing Based on Variational Mode Decomposition and Gath-Geva Clustering
Chen Zhang,Tao Fang +1 more
- 01 Jul 2023
TL;DR: In this article , a method for identifying the damage degree of rolling bearing based on the combination of Variational Mode Decomposition (VMD) and Gath-Geva (GG) fuzzy clustering is proposed.
References
Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization
TL;DR: Wang et al. as mentioned in this paper proposed a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization, which is suitable for matrix processing but challenged by the higher-order data.
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Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis.
TL;DR: A multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR) that can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal.