This study presents an in-depth analysis and prediction of the remaining life of abrasive belts under varying process parameters, based on the identification of their current wear status. A comprehensive approach combining theoretical insights and experimental methodologies was employed to investigate the wear patterns of abrasive belts throughout their entire lifecycle, as well as the influence of process parameters on belt wear. This led to the establishment of a quantitative wear rate model for abrasive belts. Utilizing this model, a abrasive belt wear process model was developed, incorporating Monte Carlo simulation methods to calculate the belts' remaining life. To further enhance the precision in monitoring the wear status and predicting the remaining life of the abrasive belts, this research integrates particle filtering techniques with the wear status monitoring model developed in the preceding chapter. The effectiveness and accuracy of the combined model were assessed and validated through experimental data, providing a significant contribution to the field of predictive maintenance and wear analysis in industrial applications.

Xuemin Liu. Research on remaining useful life prediction of abrasive belt for the rail grinding. International Journal of Frontiers in Engineering Technology (2024), Vol. 6, Issue 1: 71-76. https://doi.org/10.25236/IJFET.2024.060112.

[1] Bundscherer, M., Schmitt, T., Bayerl, S., Auerbach, T., & Bocklet, T., 2022. An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders. 2022 IEEE Sensors, pp. 1-4.

[2] Zhang, J., Yang, Y., Luo, B., Liu, H., & Li, L., 2021. Research on wear characteristics of abrasive belt and the effect on material removal during abrasive of medium density fiberboard (MDF). European Journal of Wood and Wood Products, 79, pp. 1563 - 1576. 

[3] Pandiyan, V., Caesarendra, W., Tjahjowidodo, T., & Tan, H., 2018. In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. Journal of Manufacturing Processes, 31, pp. 199-213. 

[4] Du, Y., Sun, X., Luo, B., Li, L., & Liu, H., 2022. Research on Failure Mechanism of Abrasive Belt and Effect on Abrasive of Medium-Density Fiberboard (MDF). Coatings.

[5] Li, L., Ren, X., Feng, H., Chen, H., & Chen, X., 2021. A novel material removal rate model based on single grain force for robotic belt grinding. Journal of Manufacturing Processes, 68, pp. 1-12. 

[6] Yang, Y., Guo, Y., Huang, Z., Chen, N., Li, L., Jiang, Y., & He, N., 2019. Research on the milling tool wear and life prediction by establishing an integrated predictive model. Measurement.  

[7] Romek, D., Ulbrich, D., Selech, J., Kowalczyk, J., & Wlad, R., 2021. Assessment of Padding Elements Wear of Belt Conveyors Working in Combination of Rubber–Quartz–Metal Condition. Materials, 14.