O
Orclever
Back to Journal
Research Article Open AccessOrclever Native

A Data Fusion Method Combining Image, Sensor, and Survey Data for Efficiency and Usability Analysis of Electric Power Tools in Industrial Environments

Kader Nikbay Oylum1✉,
Turgay Tugay Bilgin2
1Trex Dijital Akıllı Üretim Sistemleri A.Ş.
2Bursa Technical University
Published:June 28, 2025
DOI: 10.56038/ejrnd.v5i1.636
Vol. 5, No. 1

Abstract

The increasing integration of advanced technologies and automation in industrial production has heightened the importance of operational efficiency and safety. Among the critical components influencing workforce performance and product quality is the effective use of electric hand tools. However, the limited availability of comprehensive datasets and the absence of robust labeling methodologies present significant challenges for accurate data analysis and predictive modeling. This study addresses these limitations by incorporating field-collected data and multiple data acquisition techniques to identify relevant features for machine learning applications. An initial dataset comprising 51 attributes was systematically reduced to 16 through feature selection processes, enhancing its suitability for subsequent computational modeling. Several classification algorithms were evaluated for data labeling, with the Decision Tree method demonstrating superior performance in terms of accuracy. Despite these promising results, the dataset’s limited sample size (64 individuals) restricts the generalizability and reliability of machine learning outcomes. To mitigate this constraint, data augmentation techniques will be employed to generate synthetic instances, thereby expanding the dataset. Upon achieving a sufficient sample size, machine learning models will be developed to predict individuals’ proficiency with electric hand tools. This research contributes to the foundational knowledge required for efficient data collection, accurate labeling, and the development of predictive models in industrial settings.

Keywords
Data FusionMachine LearningDigitizationData LabelingPredictive Modeling

References

  1. 1.Krushnasamy, V. S., & Rashinkar, P. (2017). An overview of data fusion techniques. In *2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)* (pp. 694–697). IEEE.
  2. 2.Chen, X., Chen, C., & Lu, X. (2024). Algebraic method for multisensor data fusion. *PLOS ONE, 19*(9), e0307587.
  3. 3.Zhang, X., Wang, J., Huang, Y., & Zhu, F. (2024). A novel industrial big data fusion method based on Q-learning and cascade classifier. *Computer Science and Information Systems, 21*(4), 1629–1649.
  4. 4.Zhang, Q., Zhang, Y., Bao, F., Ning, Y., Zhang, C., & Liu, P. (2025). Graph-based stock prediction with multisource information and relational data fusion. *Information Sciences, 690*, 121561.
  5. 5.Adade, S. Y.-S. S., Lin, H., Johnson, N. A. N., Nunekpeku, X., Aheto, J. H., Ekumah, J.-N., ... Chen, Q. (2025). Advanced food contaminant detection through multi-source data fusion: Strategies, applications, and future perspectives. *Trends in Food Science & Technology, 156*, 104851.
  6. 6.Niu, Y., Li, Z., Li, J., & Sun, B. (2025). Accelerometer-assisted computer vision data fusion framework for structural dynamic displacement reconstruction. *Measurement, 242*, 116021.
  7. 7.Çengiz, B., & Daş, R. (2022). Veri füzyonu: Veri kaynakları, mimariler, zorluklar ve çözüm yaklaşımları. *Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34*(2), 899–922.
  8. 8.Oltrogge, D., & Kerr, E. (2024). Space traffic management: Data fusion. *Acta Astronautica.*
  9. 9.Er, A. G., Ding, D. Y., Er, B., Uzun, M., Cakmak, M., Sadee, C., ... Gevaert, O. (2024). Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: A COVID-19 cohort study. * Digital Medicine, 7*, 117.
  10. 10.Xia, X., Zhu, C., Zhong, F., & Liu, L. (2024). TransCDR: A deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion. *BMC Biology, 22*, 227.
  11. 11.Peng, Y., Zhang, W., Chen, X., Li, T., & Wang, H. (2025). Multi-source data fusion for intelligent diagnosis based on generalized representation. *Expert Systems with Applications, 228*, 121968.
  12. 12.Bhowmik, T., Iraganaboina, N. C., & Eluru, N. (2024). A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy consumption. *PLOS ONE, 19*(11).
  13. 13.Anand, S., & Sonkar, S. K. (2024). Multi-source data fusion for wind farm power forecasting through AI. *Journal of the Balkan Tribological Association, 30*(3), 439–450.
  14. 14.O’Regan, A. C., Grythe, H., Hellebust, S., Lopez-Aparicio, S., O’Dowd, C., Hamer, P. D., ... Nyhan, M. M. (2024). Data fusion for enhancing urban air quality modeling using large-scale citizen science data. *Sustainable Cities and Society, 116*, 105896.
  15. 15.Yang, R., Zhang, Y., Wei, Q., Liu, F., & Li, K. (2024). Comparison of two data fusion methods from Sentinel-3 and Himawari-9 data for snow cover monitoring in mountainous areas. *Research in Cold and Arid Regions.*
  16. 16.Qian, X., Xu, L., & Cui, X. (2024). Steady-state detection of evaporation process based on multivariate data fusion. *PLOS ONE, 19*(9).
  17. 17.Chen, F., Shang, D., Zhou, G., Ye, K., & Wu, G. (2024). Multi-source data fusion for vehicle maintenance project prediction. *Future Internet, 16*, 371.
  18. 18.Wang, C., Yao, J., Zhang, X., Wu, Y., Liu, X., Liu, H., ... Xin, J. (2024). Fatigue life data fusion method of different stress ratios based on strain energy density. *Materials, 17*, 2982.
  19. 19.Khaleghi, B., Razavi, S. N., Khamis, A., Karray, F. O., & Kamel, M. (2009). Multisensor data fusion: Antecedents and directions. In *2009 International Conference on Signals, Circuits and Systems*. IEEE.
  20. 20.Zou, X., Yan, Y., Hao, X., Hu, Y., Wen, H., Liu, E., ... Liang, Y. (2025). Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook. *Information Fusion, 113*, 102606.
Download PDF
Cite This Article
Oylum, K. N., Bilgin, T. T. (2025). A Data Fusion Method Combining Image, Sensor, and Survey Data for Efficiency and Usability Analysis of Electric Power Tools in Industrial Environments. *The European Journal of Research and Development*, 5(1), 68-83. https://doi.org/10.56038/ejrnd.v5i1.636

Bibliographic Info

JournalThe European Journal of Research and Development
Volume5
Issue1
Pages68–83
PublishedJune 28, 2025
eISSN2822-2296