CAJ | 학술논문

利用数据挖掘技术对设备监测数据进行分析，可以建立较准确的故障诊断及预警模型，但随着故障数据库的扩大，如何利用新增数据进行快速诊断成为急需解决的问题。针对上述问题，提出了加权关联规则增量更新模型，该模型直接对新增数据进行频繁项集挖掘，在一定程度上缩减了矩阵规模。通过算例证明了其挖掘结果的准确率明显优于经典的增量模型-FUP。
이용수거알굴기술대설비감측수거진행분석，가이건립교준학적고장진단급예경모형，단수착고장수거고적확대，여하이용신증수거진행쾌속진단성위급수해결적문제。침대상술문제，제출료가권관련규칙증량경신모형，해모형직접대신증수거진행빈번항집알굴，재일정정도상축감료구진규모。통과산예증명료기알굴결과적준학솔명현우우경전적증량모형-FUP。
The fault diagnosis and warning model could be built more accurate with equipment monitoring data analyzed by the data mining technology. But, as the size of fault database scale up, the problem of how to use the incremental data for rapid diagnosis needs to solve urgently. Thus, an incremental updating model based on the matrix-count algorithm is proposed to solve this problem. The model can find the large itemsets in the incremental database directly and the size of matrix is reduced. The example proves that the model is more accuracy than the classic incremental updating model-FUP.