An AI-assistant health state evaluation method of sensing devices

doi:10.1007/s40436-024-00517-w

Advances in Manufacturing ›› 2025, Vol. 13 ›› Issue (3): 539-551.doi: 10.1007/s40436-024-00517-w

• • 上一篇

An AI-assistant health state evaluation method of sensing devices

Le-Feng Shi^1,2, Guan-Hong Chen^1,2, Gan-Wen Chen³

1. National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, 401331, People's Republic of China;
2. School of Economics and Management, Chongqing Normal University, Chongqing, 401331, People's Republic of China;
3. School of Computing and Information Technology, Chongqing Normal University, Chongqing, 401331, People's Republic of China

收稿日期:2023-09-14 修回日期:2023-11-30 发布日期:2025-09-19
通讯作者: Le-Feng Shi,E-mail:shilefeng@foxmail.com E-mail:shilefeng@foxmail.com
作者简介:Le-Feng Shi is a professor at National Center for Applied Mathematics in Chongqing and becomes the team head of the Center for Intelligent Energy Management and Applications since 2018. Before working at National Center for Applied Mathematics in Chongqing, he worked as a postdoctoral researcher in China’ National State Grid from 2013 to 2016 and then as a teacher in Economics and Management School of Chongqing Normal University from 2016 to 2021. Prof. Shi excels in analyzing issues using game theory and optimization theory, especially in the field of power system and transportation. Now, he is focusing on developing a theory to characterize the series changes from a cyber-physical-social system perspective and to reveal the inner mechanism.
Guan-Hong Chen is a master degree candidate at the School of Economics and Management, Chongqing Normal University, China. His main research interests include active operation of devices and machine learning methods since 2021.
Gan-Wen Chen is a master degree candidate at the School of Computer and Information Science, Chongqing Normal University, China. His main research interests include equipment failure root cause diagnosis and machine learning methods since 2021.
基金资助:
This project was fully funded by the National Key R&D Program of China (Grant No. 2023YFA1011303), the Key Projects of Scientific and Technological Research of Chongqing Municipal Education Commission (Gant No. KJZD-K202300510).

An AI-assistant health state evaluation method of sensing devices

Le-Feng Shi^1,2, Guan-Hong Chen^1,2, Gan-Wen Chen³

1. National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, 401331, People's Republic of China;
2. School of Economics and Management, Chongqing Normal University, Chongqing, 401331, People's Republic of China;
3. School of Computing and Information Technology, Chongqing Normal University, Chongqing, 401331, People's Republic of China

Received:2023-09-14 Revised:2023-11-30 Published:2025-09-19
Supported by:
This project was fully funded by the National Key R&D Program of China (Grant No. 2023YFA1011303), the Key Projects of Scientific and Technological Research of Chongqing Municipal Education Commission (Gant No. KJZD-K202300510).

摘要/Abstract

摘要： The health states of sensing devices have a long-reaching influence on many smart application scenarios, such as smart energy and intelligent manufacturing. This paper proposes an ensemble methodology of the health-state evaluation of sensing devices, based on artificial intelligence (AI) technologies, which firstly takes into the operational characteristics, then designs a method of scenario identification to extract the typical scenarios, and subsequently puts forth a specific health-state evaluation. This method could infer the causalities of faulty devices effectively, which provides the interpretable basis for the health-state evaluation and enhances the evaluation accuracy of the health states. The suggested method has the promising potential to support the efficiently fine management of sensing devices in smart age.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00517-w

关键词: Sensing devices, Scenario identification, Health state evaluation, Artificial intelligence (AI)

Abstract: The health states of sensing devices have a long-reaching influence on many smart application scenarios, such as smart energy and intelligent manufacturing. This paper proposes an ensemble methodology of the health-state evaluation of sensing devices, based on artificial intelligence (AI) technologies, which firstly takes into the operational characteristics, then designs a method of scenario identification to extract the typical scenarios, and subsequently puts forth a specific health-state evaluation. This method could infer the causalities of faulty devices effectively, which provides the interpretable basis for the health-state evaluation and enhances the evaluation accuracy of the health states. The suggested method has the promising potential to support the efficiently fine management of sensing devices in smart age.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00517-w

Key words: Sensing devices, Scenario identification, Health state evaluation, Artificial intelligence (AI)

Le-Feng Shi, Guan-Hong Chen, Gan-Wen Chen. An AI-assistant health state evaluation method of sensing devices[J]. Advances in Manufacturing, 2025, 13(3): 539-551.

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An AI-assistant health state evaluation method of sensing devices

An AI-assistant health state evaluation method of sensing devices

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