Remaining useful life prediction of milling cutters based on long-term data sequence and parallel fully convolutional feature learning

Liang Chen, Hongli Gao (), Liang Guo, Yi Sun, Yuncong Lei and Junhua Liang
Additional contact information
Liang Chen: Southwest Jiaotong University
Hongli Gao: Southwest Jiaotong University
Liang Guo: Southwest Jiaotong University
Yi Sun: Southwest Jiaotong University
Yuncong Lei: Southwest Jiaotong University
Junhua Liang: Sichuan Polytechnic University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 6, No 34, 4365-4387

Abstract: Abstract Multi-sensor time-series signals demonstrate long-term dependence along the temporal dimension, and exhibit significant data distribution discrepancies between sensor channels. However, existing remaining useful life (RUL) prediction methods do not fully utilize this information. To solve this problem, a new deep feature learning network named parallel fully convolutional feature learning network (PFCFLN) is proposed in this paper for RUL prediction of milling cutters. In the proposed PFCFLN, multiple parallel fully convolutional subnets with different scales and layers are first employed to learn local features from long-term multi-sensor data sequences. Secondly, long-term dependence between local features is captured through the long-term attention mechanism, and local feature expression is enhanced through short-term attention and channel attention. Finally, the RUL of the milling cutter is predicted in the regression layer using attention-weighted features. In addition, a training method and an architecture search method are also presented to improve the practicality and prediction accuracy of the PFCFLN. To evaluate the proposed PFCFLN and related methods, multi-sensor data was collected from full-life testing of the milling cutter. Evaluation experiments were conducted on these data, and also compared with five advanced prediction methods. Experimental results proved the superiority of the proposed PFCFLN in improving milling cutter RUL prediction accuracy.

Keywords: Remaining useful life; Attention mechanism; Network architecture search; Milling cutter; Feature learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02459-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02459-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02459-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().