Classification of 8x8 wheeled forwarders based on cluster analysis in principal component space

The aim of the study is to develop a classification of wheeled forwarders, taking into account an extended list of operational characteristics. The material for the classification is the information provided via official websites of manufacturers Ponsse, Rottne, Komatsu, John Deere, Ecolog, HSM. In total, data on 39 forwarder models with were used for the analysis. The proposed classification bases on the results of the objects clustering using k-means method. For the clustering, the main components obtained as a result of a linear transformation of the original feature space were used. The calculations were performed in Maple 2017 program. Based on the data obtained, it is proposed to subdivide the forwarders into 4 classes, taking into account the values of 14 operational parameters, with the following approximate values of the main ones: “light forwarders”, weight of the machine is 11–15 tons, with a carrying capacity of 7–11 tons; engine power is approximately 120–130 kW, machines are equipped as standard with wheels with a tire width of 500–710 mm with a diameter of 1171–1340 mm; «medium forwarders», the weight of the machine is 15–20 tons, with a load capacity of 9–15 tons; engine power from 120 to 210 kW, machines are equipped as standard with wheels with a tire width of 600–710 mm with a diameter of 1171–1340 mm; «heavy forwarders», the weight of the machine is 19–24 tons, with a carrying capacity of 14–20 tons; engine power from 190 to 240 kW, machines are equipped as standard with wheels with a tire width of 710–780 mm with a diameter of 1340–1525 mm; «especially heavy forwarders», the weight of the machine is 22–29 tons, with a carrying capacity of 18–25 tons; engine power from 200 to 240 kW, machines are equipped as standard with wheels with a tire width of 710–780 mm with a diameter of 1340–1525 mm.

1. Andronov A.V., Petrosyan S.S., Egorin A.A., Ilyushenko D.A., Khitrov E.G. Classification of forwarders using clustering of data on their performance. Resources and Technology, 2021, vol. 18, no. 4, pp. 1–16. (In Russ.)

2. Anisimov G.M., Bolshakov B.M. New concepts of the theory of logging machines. St. Petersburg: LTA. 1998, p. 114. (In Russ.)

3. Duras T. Applications of Common Principal Components in Multivariate and High-Dimensional Analysis. JIBS Dissertation Series, 2019,. no. 131, p. 11.

4. Ecolog official site. URL: https://ecologforestry.com/en/products/forwarders/ (accessed January 03, 2023).

5. Jin X., Han J., Sammut C., Webb G.I. K-Means Clustering. Encyclopedia of Machine Learning. Springer, Boston, MA. 2011. p. 563.

6. Jolliffe I.T. Principal Component Analysis. Springer Series in Statistics, 2002, p. 112

7. John Deere official website. URL: https://www.deere.com/en/forwarders/ (accessed January 03, 2023).

8. Khakhina A.M. Methods for forecasting and increasing the cross-country ability of wheeled forest machines : diss. ... Dr. tech. Sciences. St. Petersburg, 2018, p. 318. (In Russ.)

9. Khitrov E.G. Comprehensive substantiation of the parameters and modes of operation of the movers of forest machines: diss. ... Dr. tech. Sciences. Voronezh, 2020, p. 319. (In Russ.)

10. Komatsu official website. URL: https://www.komatsuforest.com/forestmachines/our-forwarders (accessed January 03, 2023).

11. Official website of the HSM company. URL: https://www.hsm-forest.net/forwarders.html (accessed January 03, 2023).

12. Official website of the company Rottne. URL: https://www.rottne.com/en/skogsmaskiner/skotare/ (accessed January 03, 2023).

13. Ponsse official website. URL: https://www.ponsse.com/en/products/forwarders#/ (accessed January 03, 2023).

14. Steinley D., Brusco M. J. Initializing k-means batch clustering: A critical evaluation of several techniques. Journal of Classification, 2007, no. 24, pp. 99–121.