Regression Analysis and Evaluation of Forest Machine Maintainability Factors
- Authors: Shilovsky V.N.1, Skobtsov I.G.1, Konanov D.G.1
-
Affiliations:
- Petrozavodsk State University
- Issue: Vol 19, No 2 (2022)
- Pages: 134-146
- Section: Articles
- URL: https://journals.rcsi.science/2307-0048/article/view/356659
- DOI: https://doi.org/10.15393/j2.art.2022.6303
- ID: 356659
Cite item
Full Text
Abstract
The paper deals with the estimation of operational factors affecting forest machine maintainability. The main goal of this study is to substantiate and test the order of operational factors estimation using correlation and regression methods. A brief description of statistical methods of operational factor analysis is presented in the first part of the paper. The second part of the paper presents the obtained multiple regression equation and determined values of beta coefficients. Operation time of forest machine, staff employment period and servicing base technological infrastructure are accepted as independent variables determining servicing time. The interaction between independent variables and servicing time is presented as a multiple equation of linear regression. Pair correlation coefficients are used as indices of close linkage among the analyzed variable quantities. The system of normal equations is used to determine the regression coefficients of the linear model. The analysis of the obtained regression equation is given in the final part of the paper. The coefficient of determination is used as the accuracy and completeness criterion of factor selection. According to the obtained value of the criterion, it was concluded that the level of completeness of factor selection is sufficiently high. The statistical significance of regression coefficients is verified using Student's test. All considered factors are recognized as significant for servicing time estimation according to the results of verification. Furthermore, operation time of forest machine is recognized as the general maintenance factor affecting the duration of technical impacts. The effects of the staff employment period and the servicing base technological infrastructure differ slightly from each other; however, the servicing base technological infrastructure factor is more significant.
About the authors
Veniamin Nikolaevich Shilovsky
Petrozavodsk State University
Email: shisvetnik@yandex.ru
Igor Gennad'evich Skobtsov
Petrozavodsk State University
Email: iskobtsov@mail.ru
Dmitriy Gennad'evich Konanov
Petrozavodsk State University
Email: konanovdmitry17@gmail.com
References
Сравнительная оценка эксплуатационной технологичности лесозаготовительных машин / В. Н. Шиловский, А. В. Питухин, В. А. Кяльвияйнен, В. М. Костюкевич. Петрозаводск: Изд-во ПетрГУ, 2014. 104 с. Gulyarenko A. A. Calculation Method of the Reasonable Reliability Level Based on the Cost Criteria // Journal of Machinery Manufacture and Reliability. 2018. Vol. 47, is. 1. P. 96—103. Shilovsky V. N., Skobtsov I. G., Pitukhin E. A. Mathematical model of Multi- item Spare Parts Reservation // Safety in Aviation and Space Technologies. Lecture Notes in Mechanical Engineering Springer, Cham. 2022. URL: https//doi.org/10.1007/978-3-030-85057-9-48. Text. Image: electronic. Shilovsky V. N., Pitukhin E. A., Skobtsov I. G. Technigue for Improving the Organization of Maintenance of Transport and Technological Science // IOP Conference Series: Earth and Environmental Science. 2021. Vol. 666. URL: https//iopscience.iop.orga/article/ 10.1088/1755-1315/666/6/062089. Text. Image: electronic. Shilovsky V. N., Pitukhin E. A., Skobtsov I. G. Algorithm for the Development and Deliveryof a Multi-Item Set of Spare Parts and Maintenance Supplies for Geographically Dispersed Consumers // IOP Conference Series: Materials Science and Engineering. 2020. Vol. 753. URL: https://iopscience.iop.org/article/10.1088/1757-899X/753/8/082031. Text. Image: electronic. Lawley D., Maxwell A. Factor analysis as a statistical method. London: Butterworths Publ., 1963. 144 p. Hahn G., Shapiro S. Statistical models in engineering. New York: John Wiley & Sons Publ., 1967. 395 p. Амиров Ю. Д., Алферова Т. К., Волков П. Н. Технологичность конструкции изделия: Справочник. М.: Машиностроение, 1990. 768 с. Волков П. Н., Аристов А. И. Ремонтопригодность машин. М.: Машиностроение, 1975. 368 с. Гмурман В. Е. Теория вероятностей и математическая статистика. М.: Высш. шк., 2003. 479 с. Питухин А. В. Основы научных исследований. Петрозаводск: Изд-во ПетрГУ, 2017. 72 с. Шиловский В. Н., Скобцов И. Г. Оценка влияния эксплуатационных факторов на ремонтопригодность машин лесного комплекса // Известия Санкт-Петербургской лесотехнической академии. 2019. Vol. 229. С. 164—175.
Supplementary files
