Methodology for Assessing the Processes of the Occupational Safety Management System Using Functional Dependencies
Author | Affiliation | |
---|---|---|
Cherniak, Olena | ||
Date | Volume |
---|---|
2024 | 2 |
The existing functional dependencies between the measured values of quality indicators and their assessment on the dimensionless scale that were used to assess qualimetry objects of various nature have been analyzed. It has been shown that, as a rule, non-linear dependencies should be used for the objective assessment of qualimetry objects. The main task of the researcher is to choose the type of nonlinear dependence, this requires additional scientific research. The tool for choosing one or another non-linear dependence is the understanding of the physical essence of the qualimetry object, that is, the understanding of the regularities of the relationship between the measured value of qualimetry indicators and their assessment. For this, it is important to use methods of expert assessments, because, as a rule, such regularities are unknown. The functional dependence that is used to obtain assessments of indicators of the state of occupational safety in production is stepwise and includes a shape parameter. When the shape parameter changes, the curvature of the dependence changes, thereby changing the assessment on the dimensionless scale. This feature of the applied dependence makes it possible to develop a universal technique, that is, by changing the shape parameter, this dependence can be applied to various indicators of labor safety in any production. As an example, the article examines the machine-building industry and assesses the most dangerous factors. A step-by-step technique for determining a complex indicator of occupational safety in production has been developed, and its effectiveness and universality have been shown by an example of measured numerical values of dangerous factors.
Journal | Cite Score | SNIP | SJR | Year | Quartile |
---|---|---|---|---|---|
Lecture Notes in Networks and Systems | 0.9 | 0.282 | 0.171 | 2023 | Q4 |