Preventive maintenance of infant incubators based on quality control data trend analysis using an improved deep learning network(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第7期
- Page:
- 987-996
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Preventive maintenance of infant incubators based on quality control data trend analysis using an improved deep learning network
- Author(s):
- LIU Qian? ZHANG Mei2; MA Ling? XIE Yongbin? ZHAO Yuhan?/html>
- 1. Medical Equipment Office, Peking University First Hospital-Ningxia Women and Childrens Hospital, Yinchuan 750021, China
2. Emergency Department, Peking University First Hospital-Ningxia Women and Childrens Hospital, Yinchuan 750021, China 3. Medical Equipment Department, Peking University First Hospital, Beijing 100034, China
- Keywords:
- Keywords: infant incubator deep learning quality control data trend prediction preventive maintenance
- PACS:
- R318；R197.39
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.07.021
- Abstract:
- Abstract: Objective To develop an improved deep learning network to convert traditional "threshold-based" passive maintenance into "trend prediction-based" proactive intervention through the analysis of subtle changes in infant incubator quality control data, thereby optimizing preventive maintenance strategies for high-risk medical equipment. Methods Feature matrices were constructed from differences between adjacent quality control parameters of infant incubators, with data categorized into normal-condition and fault-warning classes. An improved TabNet deep learning network was developed to improve the detection of subtle parameter changes by incorporating differential-sensitive feature attention mechanisms and multi-scale feature fusion modules. Model performance was comprehensively evaluated across multiple dimensions, including classification capability, prediction accuracy, risk quantification, and decision-making value for equipment maintenance. The proposed model was then compared with original TabNet, deep neural networks, and traditional machine learning algorithms for validation. Feature importance analysis was used to identify key parameters affecting fault prediction and reveal the mechanisms underlying incubator performance degradation. Results The improved TabNet model achieved 92.3% accuracy and 75.0% recall rate on the independent test set, significantly higher than 87.5% accuracy and 7.1% recall rate of the original TabNet, while also outperforming deep neural networks and traditional machine learning methods. Decision curve analysis further confirmed its maximum net benefit across a wide range of risk thresholds. Feature importance analysis identified heating time and relative humidity as the most contributive indicators, with a cumulative contribution rate approaching 40%. Temperature abnormalities near the operation door (zone D) also exhibited significant warning value, providing crucial information for the early detection of equipment performance degradation. Conclusion A preventive maintenance prediction model for infant incubators is successfully constructed based on an improved deep learning network, enabling early identification of progressive equipment performance degradation, and transforming medical equipment management from passive troubleshooting to proactive intervention, with significant value for safeguarding the safety of premature infants treatment environment.
Last Update: 2026-07-23