Liu, Changqing, Li, Yingguang, Hua, Jiaqi, Zhao, Zhiwei and Gao, James ORCID: 0000-0001-5625-3654 (2024) A causal based method for denoising non-homologous noises in time series manufacturing monitoring data. Journal of Manufacturing Systems, 76. pp. 92-102. ISSN 0278-6125 (Print), 1878-6642 (Online) (doi:https://doi.org/10.1016/j.jmsy.2024.07.008)

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Abstract

In manufacturing process monitoring, the obtained data is always affected by multi-noise resources with different statistics features, including non-homologous noises, which adversely affect data analysis, especially the performance of data driven models which are increasingly developed in manufacturing applications. Due to the lack of prior knowledge of the noises, traditional denoising methods based on modeling noise distribution with statistical features have major limitations in denoising non-homologous noises. To address this issue, this paper presents a denoising method for monitoring data with non-homologous noises based on causal inference. The causal relationship between raw data from multi sources, and the noises and monitoring data from sensors is modeled. To remove the influence of noises on monitoring data, the instruments variable is introduced into causal model, which forming new back-door paths between non-homologous noises sources. Then the noise could be denoised according to the prior causal knowledge of the causal model. The method is verified in both simulation and actual machining environment, which lays a data foundation for establishing accurate and stable prediction and control models during manufacturing processes.

Item Type:	Article
Uncontrolled Keywords:	data denoising, non-homologous noise, causal-based method, back-door path
Subjects:	T Technology > T Technology (General) T Technology > TS Manufactures
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	28 Aug 2024 14:45
URI:	http://gala.gre.ac.uk/id/eprint/47646

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