Risk Analysis by a Probabilistic Model of the Measurement Process

Toczek, Wojciech; Smulko, Janusz

doi:10.3390/s21062053

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…In this research, based on the activities described, it was possible to propose measures to improve the hot rolling process. Knowledge of the organization and analytical methods were used [33]. Statistical analyses of the factors that may have caused the occurrence of rolled-in foreign material were performed to achieve the stated objective.…”

Section: Discussionmentioning

confidence: 99%

Improving Quality in the Process of Hot Rolling of Steel Sheets

2021

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The hot rolling of steel sheets is a highly energy-intensive process. There are technical and operational issues associated with this process, and the causes of these issues can be various. This study involved analysis of one issue that has a great influence on the resulting surface quality of rolled sheet metal: rolled foreign material. After the sheet cools, rolled foreign material tends to fall off and a hole then remain on the surface of the sheet. This paper focuses on the search for the root causes of the occurrence of foreign material rolling. The basic categorization of the causes of this issue was performed by experienced long-term operators. The 4M method (man, machine, method, and material) was used to categorize the causes. Pairwise comparison was used to verify the result. Using energy dispersive spectroscopy analysis, the origin of the foreign material was identified. The analysis confirmed that the foreign material was not derived from the primary material. Further research showed that the cause of the issue was the guide rulers, which are a structural part of the rolling mill. Measures were taken to significantly reduce the incidence of the problem, which also had the effect of reducing financial losses, which fell by a third in 18 weeks. In the future, it will be necessary to make design changes (modernization of the rolling mill), which will, however, require more financial investment.

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Section: Discussionmentioning

confidence: 99%

Improving Quality in the Process of Hot Rolling of Steel Sheets

2021

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“…Subsequently, histograms can represent graphically this probability density function, and the guard bands help us to define if the item of interest meets a specified requirement. Finally, Bayes’ theorem can be used to estimate the risk of the consumer [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Risk Assessment in Monitoring of Water Analysis of a Brazilian River

et al. 2022

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This study aimed to introduce non-parametric tests and guard bands to assess the compliance of some river water properties with Brazilian environmental regulations. Due to the heterogeneity of the measurands pH, Biochemical Oxygen Demand (BOD), manganese molar concentration, and Escherichia coli, which could be wrongly treated as outliers, as well as the non-Gaussian data, robust methods were used to calculate the measurement uncertainty. Next, based on guard bands, the compliance assessment was evaluated using this previous uncertainty information. For these four measurands, partial overlaps between their uncertainties and the specification limit could generate doubts about compliance. The non-parametric approach for calculating the uncertainty connected to the guard bands concept classified pH and BOD as “conform”, with a risk to the consumer of up to 4.0% and 4.9%, respectively; in contrast, manganese molar concentration and Escherichia coli were “not conform”, with a risk to the consumer of up to 25% and 7.4%, respectively. The methodology proposed was satisfactory because it considered the natural heterogeneity of data with non-Gaussian behavior instead of wrongly excluding outliers. In an unprecedented way, two connected statistical approaches shed light on the measurement uncertainty in compliance assessment of water analysis.

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“…Recent studies have shown that risk assessment models can be compared using metrics relates to the confusion matrix [18,34]. Expressions for evaluating the global risk of producers and consumers from equations (28) and (31) are a kind of classifier that sorts measurements as falsely rejected (FR) and falsely accepted (FA), so that R P = FR and R C = FA. Considering that the measurements within the tolerance interval and the acceptance interval were defined as true positive (TP), and that the measurements outside the acceptance interval and tolerance interval were defined as true negative (TN), it is feasible to construct a confusion matrix [18].…”

Section: Comparison Of Models Using Metrics Related To the Confusion ...mentioning

confidence: 99%

“…Knowledge about these data is given by prior distribution, that is, by the probability density function (PDF), designated with g 0 (η) [13]. Depending on the available data, the prior can be a non-parametric, one-parameter, or two-parameter distribution [27,28]. According to the principle of maximum entropy (PME), if data for two moments are available, a two-parameter distribution is used for the prior [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment for Linear Regression Models in Metrology

Božić,

Runje,

Razumić

2024

Applied Sciences

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The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers’ calculations. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer’s risk. If a product does not meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with upwards openings. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties were determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model’s performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

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Risk Analysis by a Probabilistic Model of the Measurement Process

Cited by 4 publications

References 31 publications

Improving Quality in the Process of Hot Rolling of Steel Sheets

Improving Quality in the Process of Hot Rolling of Steel Sheets

Risk Assessment in Monitoring of Water Analysis of a Brazilian River

Risk Assessment for Linear Regression Models in Metrology

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