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Journal of Intelligent Manufacturing

Erschienen in:

21.05.2021

Failure prediction in production line based on federated learning: an empirical study

verfasst von: Ning Ge, Guanghao Li, Li Zhang, Yi Liu

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 8/2022

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Abstract

Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning model without sharing data. Nevertheless, there is very few research works on FL in intelligent manufacturing. This paper presents the results of an empirical study on failure prediction in the production line based on FL. This paper (1) designs Federated Support Vector Machine and federated random forest algorithms for the horizontal FL and vertical FL scenarios, respectively; (2) proposes an experiment process for evaluating the effectiveness between the FL and CL algorithms; (3) finds that the performance of FL and CL are not significantly different on the global testing data, on the random partial testing data, and on the estimated unknown Bosch data, respectively. The fact that the testing data is heterogeneous enhances our findings. Our study reveals that FL can replace CL for failure prediction.

Vorheriger Artikel Automated visual detection of geometrical defects in composite manufacturing processes using deep convolutional neural networks

Nächster Artikel A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization

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Titel: Failure prediction in production line based on federated learning: an empirical study
verfasst von: Ning Ge
Guanghao Li
Li Zhang
Yi Liu
Publikationsdatum: 21.05.2021
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 8/2022
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-021-01775-2

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