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Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
KTH, School of Industrial Engineering and Management (ITM), Production engineering.ORCID iD: 0000-0001-8679-8049
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2024 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 19-38Article in journal (Refereed) Published
Abstract [en]

Accurately predicting the tool remaining useful life (RUL) is critical for maximizing tool utilization and saving machining costs. Various physical model-based or data-driven prediction methods have been developed and successfully applied in different machining operations. However, many uncertain factors affect tool RUL during the cutting process, making it challenging to create a precise physical model to characterize the degradation of tool performance. The success of the purely data-driven technique depends on the amount and quality of the training samples, it does not consider the physical law of tool wear, and the interpretability of the prediction results is poor. This paper presents a data-model linkage approach for tool RUL prediction based on deep feature fusion and Wiener process to address the above limitations. A convolutional stacked bidirectional long short-term memory network with time-space attention mechanism (CSBLSTM-TSAM) is developed in the data-driven module to fuse the multi-sensor signals collected during the cutting process and then obtain the mapping relationship between signal features and tool wear values. In the physical modeling module, a three-stage tool RUL prediction model based on the nonlinear Wiener process is established by considering the evolution law of different wear stages and multi-layer uncertainty, and the corresponding probability density function is derived. The real-time estimated tool wear of the data-driven module is used as the observed value of the physical model, and the model parameters are dynamically updated by the weight-optimized particle filter (WOPF) algorithm under a Bayesian framework, thereby realizing the data-model linkage tool RUL prediction. Milling experiments demonstrate that the proposed method not only improves RUL prediction accuracy, but also has good generalization ability and robustness for prediction tasks under different working conditions.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 73, p. 19-38
Keywords [en]
Data-model linkage, Feature fusion, Remaining useful life prediction, Tool wear, Weight-optimized particle filter, Wiener process
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-342827DOI: 10.1016/j.jmsy.2024.01.008Scopus ID: 2-s2.0-85183028791OAI: oai:DiVA.org:kth-342827DiVA, id: diva2:1833350
Note

QC 20240201

Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-02-01Bibliographically approved

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Wang, Lihui

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