PROBABILISTIC MATHEMATICAL MODEL FOR QUANTIFYING THE QUALITY OF LARGE ELECTRIC MACHINES IN THE CONTEXT OF KNOWLEDGE MANAGEMENT
Authors:
1.
Benderskiy politehnicheskiy filial GOU «Pridnestrovskiy gosudarstvennyy universitet»
Russian Federation
Russian Federation
Type:
Сonference article
Published:
13.04.2020
Subject area:
UDK 65 Управление предприятиями. Организация производства, торговли и транспорта
GRNTI 82.05 Теория и методология управления
GRNTI 82.05 Теория и методология управления
Language:
Russian
Keywords:
quality assessment, knowledge management, production of electric machines, generalized desirability function
Abstract (English):
As a result of the analysis, objective indicators of the quality of large electric machines were determined, as well as their weights were established. Based on the Harrington-Mencher function, the obtained CAM quality indicators are integrated into a single quantitative assessment. Special quality criteria (di) and the generalized Harrington-Mencher function (Dj) are considered as an integral quantitative assessment of CAM quality.
As a result of the analysis, objective indicators of the quality of large electric machines were determined, as well as their weights were established. Based on the Harrington-Mencher function, the obtained CAM quality indicators are integrated into a single quantitative assessment. Special quality criteria (di) and the generalized Harrington-Mencher function (Dj) are considered as an integral quantitative assessment of CAM quality.
Keywords:
quality assessment, knowledge management, production of electric machines, generalized desirability function
quality assessment, knowledge management, production of electric machines, generalized desirability function
1. Radchenko V.N, Fedorova T.A. Sostoyanie metodicheskogo i normativnogo obespecheniya sistemy menedzhmenta kachestva mashinostroitel'nyh predpriyatiy // Nauka i biznes: puti razvitiya. 2019. №6(96). S 85-88.
2. Fedorova T.A., Baeva T.Yu. Vzaimosvyaz' instrumentov upravleniya kachestvom i menedzhmenta znaniy // Colloquium-journal. Warszawa, Poland: Wydrukowano w «Chocimska» 24, 2019. S. 59-62.
3. Fedorchenko S.G., Dolgov Iu.A., Kirsanova A.V. Obobschennaia functsia poleznosti i ee prilojenia -Tiraspoli, 2011. -196 p
4. Dolgov Yu.A. Statisticheskoe modelirovanie: Uchebnik dlya vuzov. -2-e izd. Dop. -Tiraspol': Poligrafist, 2011.-346 s.
