A fault diagnosis approach for roller bearings based on EMD method and AR model

Cheng, Junsheng; Yu, Dejie; Yu, Yang

doi:10.1016/j.ymssp.2004.11.002

Cited by 195 publications

(81 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The network was employed as a classification model for machine fault diagnosis. In [10], the authors utilized the EMD algorithm to decompose vibration data from defect roller bearings into IMFs. The autogressive models (AR) were established for each IMF to extract AR parameters and residual variations, which were then aggregated into one feature vector.…”

Section: Adaptive Empirical Mode Decomposition For Bearing Fault Detementioning

confidence: 99%

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Tuan

Nguyen

2016

SV-JME

View full text Add to dashboard Cite

show abstract

Section: Adaptive Empirical Mode Decomposition For Bearing Fault Detementioning

confidence: 99%

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Tuan

Nguyen

2016

SV-JME

View full text Add to dashboard Cite

show abstract

“…SVM has good generalization performance on small size sample and nonlinear problems [8] . For the training set: T={(x1, y1), (x2, y2),…,( xn, yn)}.…”

Section: The Basic Principlementioning

confidence: 99%

“…The difference of two type samples number is l. Solving the quadratic programming problem and the optimal Lagrange coefficients{α1, α2,…,αn} corresponding to the samples in the unbalanced sample set can be obtained; Define the crossover operator  and mutation operator  , calculated by equation (8)(9)(10)(11)(12).…”

Section: Realization Of Unbalanced Sample Diagnostic Modelmentioning

confidence: 99%

Research on Sample Dataset Balance Method of SVM Based on GA

Han¹

2016

Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering

View full text Add to dashboard Cite

“…Thus, some recent research efforts are paid on the fault diagnosis method combining time series model with machine learning method [2]. Machine learning based feature extraction shows better performance than spectral estimation method when analyzes vibration signal [3]. However, there are only a few researches and applications on it over AE signal.…”

Section: Introductionmentioning

confidence: 99%

A Fault Degree Classification Method for AE Signal Based on VAR-DBN

Zhou¹,

Li²,

Li³

2018

dtcse

View full text Add to dashboard Cite

Abstract. It is difficult to find the train axle fault earlier by traditional calculation method. In this paper, a time series analysis and pattern recognition analysis based model is presented to improve the performance on diagnosing fault of AE signal. The suggested methodology mainly involves three parts. First, empirical mode decomposition (EMD) is used to transform the AE signals into stationary signals by decomposing the original signal into several intrinsic mode functions (IMFs) and one residue. Then, vector autoregressive (VAR) model is selected to reflect the characteristic included in IMFs by establishing feature vector comprised by the coefficients of the VAR model. In the end, a Deep Belief Network(DBN) is introduced to classify the AE signals and detect the fault signals caused by faults. The result of the experiment shows that compared with other time series parameter analysis based classification study, the proposed method has better behavior in accuracy when identifies different conditions of railway vehicle axle on AE signal.

show abstract

A fault diagnosis approach for roller bearings based on EMD method and AR model

Cited by 195 publications

References 9 publications

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Research on Sample Dataset Balance Method of SVM Based on GA

A Fault Degree Classification Method for AE Signal Based on VAR-DBN

Contact Info

Product

Resources

About