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| Multi-modal Health Monitoring and Dynamic Evaluation Method for Liquid Cooling Systems |
| Received:March 04, 2025 Revised:March 26, 2025 |
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| DOI：10.7643/issn.1672-9242.2025.05.019 |
| KeyWord:liquid cooling system health management multi-modal sensing condensation detection entropy weight method dynamic evaluation |
| Author | Institution |
| CHEN Xuewen |
29th Research Institute of CETC, Chengdu , China |
| WANG Yibo |
29th Research Institute of CETC, Chengdu , China |
| KE Changyan |
29th Research Institute of CETC, Chengdu , China |
| HE En |
29th Research Institute of CETC, Chengdu , China |
| XING Kai |
29th Research Institute of CETC, Chengdu , China |
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| Abstract: |
| The work aims to propose a multi-modal health monitoring and dynamic evaluation method for liquid cooling systems to address the challenges of concealed condensation, low detection sensitivity, and high safety risks in liquid-cooled electronic equipment during long-term operation, where existing methods suffer from insufficient dynamic monitoring and lack of multi-parameter collaborative analysis. A distributed multi-physics monitoring network was constructed by integrating ion-sputtered liquid sensors, pressure sensors, differential pressure sensors, and PT1000 platinum resistance temperature probes, enabling real-time tracking of condensation accumulation, flow channel pressure changes, flow channel blockage rate, and heat exchange efficiency. A fuzzy comprehensive evaluation model (HIS-LC) was innovatively established, utilizing the entropy weight method to dynamically allocate feature weights (ω1–ω4) and quantify system health status on a 0-100 scale. Experimental results demonstrated that the threshold of condensation detection was 0.05 mL (95% confidence), the detection sensitivity of 1 mL condensation was 98%, the pressure transient response time was 2 s, and the alarm rate was 100% when the flow channel pressure difference was greater than 1.5 MPa. Continuous 48-hour operation tests confirmed the cooling system stable health scores≥98 (standard deviation σ=0.8) and a heat dissipation efficiency decay rate <0.8%/24 h. This research provides technical support for transitioning high-reliability liquid cooling systems from “post-failure maintenance” to “predictive maintenance”. |
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