Data driven health monitoring and fault diagnosis of mechanical equipment

ZHANG Meihua; TAO Ran

doi:10.12299/jsues.24-0180

Volume 39 Issue 4

Dec. 2025

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Article Navigation > Journal of Shanghai University of Engineering Science > 2025 > 39(4): 435-441

ZHANG Meihua, TAO Ran. Data driven health monitoring and fault diagnosis of mechanical equipment[J]. Journal of Shanghai University of Engineering Science, 2025, 39(4): 435-441. doi: 10.12299/jsues.24-0180

Citation:

ZHANG Meihua, TAO Ran. Data driven health monitoring and fault diagnosis of mechanical equipment[J]. Journal of Shanghai University of Engineering Science, 2025, 39(4): 435-441. doi: 10.12299/jsues.24-0180

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Data driven health monitoring and fault diagnosis of mechanical equipment

doi: 10.12299/jsues.24-0180

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2024-06-20
Available Online: 2026-02-02
Publish Date: 2025-12-01

Abstract

Abstract

To address the incomplete utilization of mechanical equipment fault data and overcome the limitations of traditional health status monitoring and fault diagnosis methods, a data-driven system architecture for health monitoring and fault diagnosis was constructed. A health status monitoring method based on digital twins was proposed, in which equipment vulnerability was incorporated to monitor the health status. Furthermore, a data-driven fault diagnosis method was studied. By utilizing a nonlinear kernel mapping algorithm, historical fault data were analyzed to determine the abnormal boundary of the equipment. Through the analysis of real-time data, equipment abnormalities and the key factors leading to these abnormalities were identified. Finally, an empirical analysis was conducted taking a robotic arm on an assembly line as a case study. The results indicate that the proposed method can effectively identify the performance parameters leading to equipment abnormalities and improve the dynamic response performance of the workshop to machine failures.
- date-driven,
- mechanical equipment,
- health status,
- fault diagnosis,
- vulnerability

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References(18)

References

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