The Effect of Steel Leveler Parameters on Vibration Feature

Nikula, Riku-Pekka; Karioja, Konsta

doi:10.3384/ecp17142433

Cited by 3 publications

(7 citation statements)

References 3 publications

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“…However, the analysis of vibration measurements from roller levelers in industrial cases has rarely been discussed in the literature. This study extends the work presented by and Nikula and Karioja (2016) by introducing a modeling approach to the stress evaluation of the industrial leveler.…”

Section: Introductionsupporting

confidence: 53%

“…It would then be certain that the effects of the idle state and possible measurement disturbances during the idle state could be avoided in the norm sums. The operational parameters of the roller leveler and steel strip properties correlate strongly (Nikula and Karioja 2016). Therefore, based on the results, it is assumed that the steel strip properties alone could be used for stress prediction.…”

Section: Fig 10mentioning

confidence: 99%

“…Additionally, analyzed vibration measurements to study the stress inflicted on an industrial roller leveler. The effects of leveling parameters on vibration features were analyzed by Nikula and Karioja (2016). Moreover, there is a broader selection of studies related to the vibrations in other steel forming processes and especially rolling processes.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

et al. 2017

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The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties.

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Section: Introductionsupporting

confidence: 53%

Section: Fig 10mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

et al. 2017

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“…These machines are exposed to high mechanical stresses [4] due to the advanced steel grades processed [5] and the increasing demands for productivity. Therefore, the monitoring of such machines becomes increasingly important, but relatively few studies deal with the condition monitoring of such machines [6]. Instead, most of the studies are concerned with process simulations, parameter analysis, and the behavior of material in the leveling process [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…In the case of roller levelers, there is, however, limited information on which features are useful for the monitoring of structural vibration when the monitoring is not targeted at a specific component, such as the bearing, shaft, or gear. Correlations between a few vibration features and machine parameters were studied in [6], but it was not shown how this information could be used in monitoring.…”

Section: Introductionmentioning

confidence: 99%

Roller Leveler Monitoring Using Acceleration Measurements and Models for Steel Strip Properties

Nikula

Leiviskä

2020

Machines

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The advanced steel grades and high productivity requirements in the modern steel industry subject production machines to increased mechanical stresses, which inflicts losses. Novel data-oriented solutions to the monitoring of machines have a pivotal role in loss prevention, but the industrial data with high sampling rates, noise, and dimensions bring challenges there. This study proposes a new monitoring approach for roller levelers based on vibration measurements and regression models for estimating steel strip properties including yield strength, width, and thickness. The regression residuals are monitored based on moving mean and range charts, which reveal changes from the expected normal operation. A high-dimensional feature set of 144,000 statistical features was studied with various feature selection methods, including filters and wrappers. Multiple linear regression and generalized regression neural network were applied in modeling. The approach was validated using data from an industrial roller leveler processing steel strips with diverse properties. The results reveal that the accurate prediction of the strip thickness from the strip properties is possible and multiple linear regression was generally the superior model therein. Additional simulations indicated that the control charts can detect deviant operation. Supplemental information about the momentary operation of the machine would improve the approach.

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