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This article discusses the performance degradation of the R2R Engineering roller, using the Principal component analysis method to extract features, and the Variance matrix. The results of this research were presented in a paper published in the Journal of Materials Science and Engineering. These results highlight the potential for improvement of the design of rolling bearings. This article is intended for people who want to design a roller bearing using computer simulation. It also contains practical advice for manufacturing engineers.

Performance Degradation of R2R Engineering Roller

During continuous operation, the rollers of an R2R Engineering web converting machine are subjected to loads and stresses. The web buckling capacity and the required pretension can be reduced by misalignment of the rollers. In addition, the web's ability to apply pre-tension decreases with increasing web width. In such circumstances, a reduction in pre-tension capability of the roller is inevitable.

This deformation is caused by the geometrical error and lateral disturbances introduced by the processing equipment. To compensate for these effects, various efforts have been made through the use of control. The mechanical perspective of R2R processing has been investigated through numerous studies. Among these studies, Kulachenko et al. (2007) developed a new shell element specifically for R2R applications and proposed a three-dimensional finite element procedure.

Principal Component Analysis Method for Feature Extraction

A three-dimensional contour map can be constructed using a Principal Component Analysis method for feature extraction in R2R Engineering rolling bearings. This data enables the determination of the intermediate and late degradation states of the roller. It is also possible to determine the overall performance degradation of a roller using a three-dimensional contour map. The information obtained by using this data enables predictive maintenance of rolling bearings.

The first step is the calculation of the eigenvalues of the vibration signal. The eigenvectors correspond to the eigenvalues. Then, the eigenvector matrix of the selected eigenvalues is calculated. The selected eigenvectors are represented in the No i column of the eigenvector matrix. The next step in the feature extraction procedure is to deduce the features that are the most important for analysis.

This paper presents a novel approach to feature extraction in R2R engineering roller bearings. The researchers used a PCA model based on the analysis of the performance influencing factors. They then constructed a PCA model based on the extracted performance indicators. The original feature parameter matrix consisted of ten dimensions and was subsequently normalized. The PCA model was able to accurately describe the performance indicators of the rolling bearings using the resulting feature parameters.

Variance Matrix of R2R Engineering Roller

The variation matrix of R2R Engineering rollers for processing of flexible materials is based on the principal component analysis (PCA). It is a mathematical approach that involves the computation of the principal components and the cumulative contribution rate. In this study, we identified three principal components that can evaluate the performance degradation of R2R processing roll for flexible material. The three components were then plotted to develop the three-dimensional contour map.

The principal component changing trend of the roll shaft was also observed. It showed a stable trend during the early and mid-degradation stages, but there was no significant increase in the middle stage. We were unable to distinguish between the mid-performance decline and the early-performance decline of the roll shaft. We therefore derived a curve by using eigenvalues and principal components. The resulting curve shows that the roll shafts can degrade at different rates and that the degradation of the performance depends on the initial conditions of the roller.

In order to extract the degradation feature of R2R engineering rollers, we proposed a PCA method. We first established the covariance matrix and feature matrices of the roll shaft vibration signal. After this, we introduced the Jacobi method to study the eigenvalues of the covariance matrix. Next, we verified the effectiveness of the proposed method by performing a series of experiments.

 

R2R Engineering

R2R Engineering

R2R Engineering

R2R Engineering

 

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