Analysis of intelligently preventive maintenance method and application effect of magnetic resonance imaging equipment based on a predictive model
10.3969/j.issn.1672-8270.2024.12.030
- VernacularTitle:基于预测性模型的磁共振成像设备智能预防性维护方法构建及使用效果分析
- Author:
Peng ZHOU
1
;
Qiong LIU
;
Wenfei XING
;
Chaozhi ZHANG
Author Information
1. 中国人民解放军总医院海南医院医学工程科 三亚 572013
- Publication Type:Journal Article
- Keywords:
Predictive model;
Magnetic resonance imaging(MRI);
Preventive maintenance;
Effectiveness analysis
- From:
China Medical Equipment
2024;21(12):167-172
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the effects of intelligently preventive maintenance of magnetic resonance imaging(MRI) equipment based on a predictive model in the intelligently preventive maintenance of medical equipment. Methods:An autoregressive integrated moving average (ARIMA) predictive model of intelligently preventive maintenance of MRI equipment was designed to perform detection for performance of MRI equipment,so as to do well for the intelligently preventive maintenance from three dimensions:preventive maintenance,fault repair and quality detection. A total of 20 MRI equipment in Hainan Hospital of Chinese PLA General Hospital from 2022 to 2023 were selected,and conventional maintenance method was adopted for equipment maintenance from January to December 2022,and intelligently preventive maintenance method (predictive model method) based on predictive model was adopted for MRI equipment based on predictive model for equipment maintenance from January to December 2023. The quality scores of preventive maintenance,the standardization level of operation and management of equipment and the satisfaction scores of operators for service quality of equipment were compared between the two maintenance methods. Results:The maintenance efficiency score,maintenance timeliness score,qualification rate of quality inspection and average score of maintenance regularity of the equipment of using maintenance method of predictive model were respectively (90.36±6.33) points,(89.14±4.36) points,(88.62±3.36) points and (91.58±3.47) points,all of which were significantly higher than those of conventional maintenance method,and the differences were statistically significant (t=10.876,11.360,12.283,12.226,P<0.05). The average operation rate,operational rate and allocation rate of equipment of using the maintenance method of predictive model were (91.58±4.12)％,(90.69±5.14)％,and (89.25±6.01)％,respectively,which were significantly higher than those of conventional maintenance method,while the fault frequency of the former was (1.02±0.25) times/year,which was significantly lower than that of conventional maintenance method,the differences were statistically significant (t=5.298,6.557,10.572,27.867,P<0.05). The average scores of the satisfaction of preventive maintenance,preventive repair,fault maintenance,post-maintenance and quality survey of 10 operators were respectively (90.54±2.36) points,(91.59±3.14) points,(92.54±4.69) points,(91.89±3.25) points and (92.54±2.45) points by using maintenance method of predictive model,which were all higher than those of conventional maintenance methods,and the differences were statistically significant (t=12.807,12.290,8.764,12.146,15.612,P<0.05). Conclusion:The predictive model-based intelligently preventive maintenance method of MRI equipment can improve the maintenance quality and operational quality of equipment,and relevant operators have a higher satisfaction for the service of equipment,which can effectively enhance the management effect for equipment and ensure the stable and efficient application of the equipment.
