基于数据挖掘的故障模式、影响及危害性分析改进方法

针对FMECA在复杂系统风险分析中存在主观性、局限性和单一性的缺点，本文提出一种基于数据挖掘的FMECA改进方法。通过数据挖掘FMECA风险清单，并利用Python和数据库优化了相应的算法。结合实例案例分析，给出了风险等级评估带状图、故障层次关系图以及改进的风险矩阵图等。结果表明：改进的FMECA能够充分利用相关数据，使得分析结果更加快速、准确和全面，有助于进一步发现设备潜在故障之间的关系，为设备的智能健康管理提供了支持。

Research on Improved Method of FMECA Based on Data Mining

Aiming at the defects of subjectivity, limitation and singleness of FMECA in risk analysis of complex system, an improved method of FMECA based on data mining is proposed. The corresponding algorithm is optimized by using Python and database through data mining FMECA risk list. The risk level assessment strip diagram, fault hierarchy diagram and improved risk matrix diagram are given combined with the case analysis. The results show that the improved FMECA can make full use of relevant data, making the analysis results more rapid, accurate and comprehensive, helping to further discover the relationship between potential failures of the equipment, and providing support for the intelligent health management of the equipment.