Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses

Moura, Elineudo Pinho de; Souto, C.R.; Silva, A.A.; Irmão, M.A.S.

doi:10.1016/j.ymssp.2010.11.021

Cited by 92 publications

(54 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, when PCA is used to denoise or for data compression, the number of effective eigenvalues is determined by the cumulative contribution rate and its deformation [9][10][11][12][13][14], expressed as…”

Section: Introductionmentioning

confidence: 99%

Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

Zhao

2018

Shock and Vibration

View full text Add to dashboard Cite

Vibration-based diagnosis has been employed as a powerful tool in maintaining the operating efficiency and safety for large rotating machinery. However, the extraction of malfunction features is not accurate enough by using traditional vibration signal processing techniques, owing to their intrinsic shortcomings. In this paper, the relationship between effective eigenvalues and frequency components was investigated, and a new characteristic frequency separation method based on PCA (CFSM-PCA) was proposed. Certain feature frequency could be purified by reconstructing the specified eigenvalues. Furthermore, three significant perspectives were studied via the distribution of effective eigenvalues, and theoretical derivations were subsequently illustrated. More importantly, this proposed scheme could also be used to synthesize axis orbits of larger machines. Purified curves were so explicit and the CFSM-PCA exhibited higher efficiency than harmonic wavelet and wavelet packet.

show abstract

Section: Introductionmentioning

confidence: 99%

Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

Zhao

2018

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Therefore, quantitative analysis of the bearing's health status plays an important role in monitoring bearing performance, providing repair and maintenance strategies. At present, the vibration signal becomes mostly used for the performance monitoring of rolling bearings, because it implies useful information reflecting the location and extent of the fault [2][3][4]. Analyzing and mining the vibration signal can help to understand the bearing status and predict the effective residual life so as to lay a foundation for health management.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing health status assessment based on ITD-GMM method

Lu¹,

Wang²

2017

Vib. proced.

View full text Add to dashboard Cite

Abstract. This paper proposed a Rolling bearing health state assessment based on ITD-GMM method to fully dig the favorable information of the vibration signal from the rolling bearing with decline trend. By data analytic, the six components of vibration signal were calculated, and each component has three feature vectors. Finally, the performance of rolling bearing was quantified, and the curve of performance was acquired. The experimental results indicate that the method is feasible and effective for the assessment of rolling bearing.

show abstract

“…PCA is a typical multivariate analysis and pattern recognition method, whose parameters limits are few and whose calculation is simple [6]. So far, multivariate statistical performance monitoring based on PCA has been widely used in research fields such as quality control, process monitoring and fault diagnosis [7][8][9][10]. In condition monitoring, statistical variable values and their control limits in different subspaces can be calculated after the most comprehensive simplification of current status sample data has been formed, based on PCA, representing the statistical characteristics of the current state.…”

Section: Introductionmentioning

confidence: 99%

Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM

et al. 2013

View full text Add to dashboard Cite

Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses

Cited by 92 publications

References 9 publications

Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

Rolling bearing health status assessment based on ITD-GMM method

Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM

Contact Info

Product

Resources

About