Article: Remaining useful life prediction for milling tool based on an improved inverse Gaussian process Journal: International Journal of Hydromechatronics (IJHM) 2025 Vol.8 No.7 pp.1 - 20 Abstract: The present work proposes a novel remaining useful life (RUL) prediction method for milling tools by improving inverse Gaussian (IG) stochastic degradation process, to address the insufficient accuracy of existing RUL predicting methods under limited training sample data. In contrast to the standard IG process, which establishes the degradation increment relative to the initial state of degradation, the proposed improved IG process adopts an assumption that the degradation increment is more greatly affected by the current state of degradation rather than the initial state. This yields a three-parameter increment of wear, and the parameters are estimated by maximum likelihood estimation. Finally, the RUL of tool is predicted based on slice sampling technology. Applications of the proposed RUL prediction method to a benchmark milling tool wear dataset and a milling tool wear experiments demonstrate that the method obtains superior RUL prediction performance relative to two other state-of-the-art methods. Inderscience Publishers - linking academia, business and industry through research

Title: Remaining useful life prediction for milling tool based on an improved inverse Gaussian process

Authors: Chen Gao; M.Z. Nuawi; J.A. Ghani; Yuqing Zhou; Jicai Wang

Addresses: School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, 314031, China; Department of Mechanical and Material Engineering, Faculty of Engineering and Build Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia ' Department of Mechanical and Material Engineering, Faculty of Engineering and Build Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia ' Department of Mechanical and Material Engineering, Faculty of Engineering and Build Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia ' College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing, 314001, China; College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China ' School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, 314031, China

Abstract: The present work proposes a novel remaining useful life (RUL) prediction method for milling tools by improving inverse Gaussian (IG) stochastic degradation process, to address the insufficient accuracy of existing RUL predicting methods under limited training sample data. In contrast to the standard IG process, which establishes the degradation increment relative to the initial state of degradation, the proposed improved IG process adopts an assumption that the degradation increment is more greatly affected by the current state of degradation rather than the initial state. This yields a three-parameter increment of wear, and the parameters are estimated by maximum likelihood estimation. Finally, the RUL of tool is predicted based on slice sampling technology. Applications of the proposed RUL prediction method to a benchmark milling tool wear dataset and a milling tool wear experiments demonstrate that the method obtains superior RUL prediction performance relative to two other state-of-the-art methods.

Keywords: milling tool; remaining useful life; inverse Gaussian process.

DOI: 10.1504/IJHM.2025.148900

International Journal of Hydromechatronics, 2025 Vol.8 No.7, pp.1 - 20

Received: 21 Apr 2025
Accepted: 16 Aug 2025
Published online: 01 Oct 2025 *

Title: Remaining useful life prediction for milling tool based on an improved inverse Gaussian process

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