Exponential degradation model based remaining life prediction for tools of milling machine.

Arifeen, Murshedul; Petrovski, Andrei; Hasan, Md. Junayed; Ahmad, Zeeshan

Abstract

Cutting tools of milling machines are prone to failure, and it is essential to predict their remaining useful life to ensure cost-effective maintenance in the manufacturing industry. Recent studies have shown that deep learning techniques can effectively predict the remaining useful life. However, training a deep learning model on a small dataset can be challenging. Moreover, generating a sufficient training dataset that includes failure samples is even more complex. Therefore, this paper proposes an exponential degradation-based technique for predicting the remaining life of cutting tools due to a small dataset. The proposed method has five main steps: data processing, feature extraction, feature selection, and feature fusion to construct health indicators. Finally, using these health indicators, an exponential degradation model can estimate the remaining life. A case study of the proposed approach is demonstrated on a recently published cutting tools dataset. The experimental results show that the exponential model does not require large-size data to predict remaining life.

Citation

ARIFEEN, M., PETROVSKI, A., HASAN, M.J. and AHMAD, Z. 2024. Exponential degradation model based remaining life prediction for tools of milling machine. In Kovalev, S., Kotenko, I., Sukhanov, A., Li, Y. and Li Y. (eds.) Proceedings of the 8th Intelligent information technologies for industry international scientific conference (IITI'24), 1-7 November 2024, Harbin, China. Lecture notes in networks and systems, 1209. Cham: Springer [online], volume 1, pages 355-365. Available from: https://doi.org/10.1007/978-3-031-77688-5_34

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 8th Intelligent information technologies for industry international scientific conference (IITI'24)
• Start Date: Nov 1, 2024
• End Date: Nov 7, 2024
• Acceptance Date: Jun 24, 2024
• Online Publication Date: Dec 19, 2024
• Publication Date: Dec 20, 2024
• Deposit Date: Apr 17, 2025
• Publicly Available Date: Dec 20, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Pages: 355-365
• Series Title: Lecture notes in networks and systems
• Series Number: 1209
• Series ISSN: 2367-3370; 2367-3389
• ISBN: 9783031776878
• DOI: https://doi.org/10.1007/978-3-031-77688-5_34
• Keywords: Milling machines; Cutting tools; Predictive maintenance; Remaining useful life (RUL); Deep learning; Exponential degradation models
• Public URL: https://rgu-repository.worktribe.com/output/2626130