移动X射线机器的故障模式识别和效应分析,使用选择的MADM技术
Vikas Sisodia1, Dharmalingam Ganesan2, Sachin Salunkhe3,4
1Department of Automation and Robotics Engineering, Prestige Institute of Engineering Management and Research, Indore, Madhya Pradesh, 452010, India.
Scientific reports
|July 8, 2025
概括
本研究介绍了移动X射线机器的新故障模式和影响分析 (FMEA) 方法,通过整合分析层次过程 (AHP) 和TOPSIS来改进风险评估. 改进的模型克服了传统的风险优先级号码 (RPN) 的局限性,以实现更准确的故障模式排名.
科学领域:
- 医疗工程和设备可靠性 医疗工程和设备可靠性
- 医疗技术中的风险管理
- 改善医疗保健服务的质量.
背景情况:
- 故障模式和影响分析 (FMEA) 对于提高医疗保健服务和产品的可靠性和安全性至关重要.
- 移动X射线设备越来越多地被采用,呈现出与流动性相关的独特故障挑战.
- 在FMEA中,传统的风险优先级号 (RPN) 有局限性,因为它对严重程度,发生率和检测具有同等权重,可能会误导实际风险.
研究的目的:
- 解决故障模式和影响分析 (FMEA) 中常规风险优先次数 (RPN) 的局限性.
- 开发和验证一个修改后的FMEA模型,集成分析层次流程 (AHP) 和以类似于理想解决方案的优先顺序 (TOPSIS) 技术.
- 提高风险评估和移动X射线机器故障模式排名的准确性和有效性.
主要方法:
- 开发了一种修改后的FMEA方法,整合了AHP来生成风险因素 (严重程度,发生,检测) 的权重.
- 关于风险因素的故障模式的得分是使用传统的RPN方法获得的.
- 应用了AHP-TOPSIS-FMEA综合方法来识别和排名移动X射线机器中与驱动相关的故障,随后进行了比较和灵敏度分析.
主要成果:
- 与传统的RPN方法相比,修改后的FMEA模型,包括AHP和TOPSIS,在排名故障模式方面表现出更好的准确性.
- 该研究提供了应用增强型FMEA对移动X射线机器驱动器故障的实际示例.
- 对比分析证实了新方法对以前多重属性决策 (MADM) 方法的优势.
结论:
- 综合的AHP-TOPSIS-FMEA方法为移动X射线机器故障的风险评估提供了更强大,更准确的方法.
- 这种增强的FMEA模型为传统的RPN提供了更好的替代方案,更好地反映了故障模式的真正风险概况.
- 这些发现支持采用这种先进的FMEA技术来提高关键医疗设备的可靠性和安全性.
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